Estimation of effectiveness of ablation adjacency

ABSTRACT

Methods for estimating of the effectiveness of catheter ablation procedures to form lesions, and particular lesions which together form an ablation segment of an ablation line. Lesion effectiveness parameters are received, and effectiveness, optionally the joint effectiveness, of corresponding ablations (optionally planned, current, and/or already performed) is estimated. In some embodiments, estimating is based on use by computer circuitry of an estimator constructed based on observed associations between previously analyzed lesion effectiveness parameters, and observed lesion effectiveness. Additionally or alternatively, estimators may be constructed based on analytic functions. The estimator is used by application to the received lesion effectiveness parameters.

RELATED APPLICATIONS

This application is a National Phase of PCT Patent Application No.PCT/IB2018/050195 having International filing date of Jan. 12, 2018,which claims the benefit of priority under 35 USC § 119(e) of U.S.Provisional Patent Application Nos. 62/445,380 and 62/445,377 both filedon Jan. 12, 2017. PCT Patent Application No. PCT/IB2018/050195 is also aContinuation-in-Part (CIP) of PCT Patent Application No.PCT/IB2017/057186 having International filing date of Nov. 16, 2017,which claims the benefit of priority under 35 USC § 119(e) of U.S.Provisional Patent Application Nos. 62/445,380 and 62/445,377 both filedon Jan. 12, 2017, and also 62/422,748 filed on Nov. 16, 2016. Thecontents of the above applications are all incorporated by reference asif fully set forth herein in their entirety.

FIELD AND BACKGROUND OF THE INVENTION

The present invention, in some embodiments thereof, relates to systemsand methods for treatment with intrabody catheters and, moreparticularly, but not exclusively, to systems and methods for estimatingprognosis of ablation treatment effectiveness, and/or planning and/ordynamically adjusting planning of treatments such as ablation treatmentsperformed using intrabody catheters based on the estimation.

Catheterized intra-body ablation probes (for example, RF ablationprobes) are in use for minimally invasive ablation procedures. Suchprocedures are performed, for example, in the treatment of cardiacarrhythmia. In the control of cardiac arrhythmia, a goal of ablation isto create lesions in a pattern which will break pathways of abnormalelectrophysiological conduction which contribute to heart dysfunction(such as atrial fibrillation).

Single procedure success rates of catheter ablation at one year appearvariable. For example, they have been reported at 15%-60% (Sohns et al.,Catheter Contact Force: A Review of Emerging Techniques and Technologiesin AF Ablation. Journal Innov Cardiac Rhythm Management, 2014;5:1773-1780).

Earlier time post-procedure success percentages are generally higher.Gaps in the ablation line have been reported to contribute torestoration of impulse conduction (Ouyang et al., Recovered pulmonaryvein conduction as a dominant factor for recurrent atrialtachyarrhythmias after complete circular isolation of the pulmonaryveins: lessons from double Lasso technique. Circulation. 2005; 111:127-135).

One form of catheter ablation known as RF ablation relies on heatingcaused by the interaction between a high-frequency alternating current(e.g., 350-500 kHz) introduced to a treatment region, and a dielectricmaterial (e.g., tissue) in the treatment region. One variable affectingthe heating is the frequency-dependent relative permittivity κ of thetissue being treated. The (unitless) relative permittivity of a material(herein, κ or dielectric constant) is a measure of how the material actsto reduce an electrical field imposed across it (storing and/ordissipating its energy). Relative permittivity is commonly expressed as

${\kappa = {{ɛ_{r}(\omega)} = \frac{ɛ(\omega)}{ɛ_{0}}}},$where ω=2πf, and f is the frequency (of an imposed voltage or signal).In general, ε_(r)(ω) is complex valued; that is:ε_(r)(ω)=ε′_(r)(ω)+iε″_(r)(ω).

The real part ε′_(r)(ω) is a measure of how energy of an appliedelectrical field is stored in the material (at a given electrical fieldfrequency), while the imaginary part ε″_(r)(ω) is a measure of howenergy is dissipated. It is this dissipated energy that is converted,for example, into heat for ablation. Loss in turn is optionallyexpressed as a sum of dielectric loss ε″_(rd) and conductivity σ as

${ɛ_{r}^{''}(\omega)} = {ɛ_{rd}^{''} + {\frac{\sigma}{\omega \cdot ɛ_{0}}.}}$

Any one of the above parameters: namely κ, ε, ε′_(r), ε″_(r), σ, and/orε″_(rd), may be referred to herein as a dielectric parameter. The termdielectric parameter encompasses also parameters that are directlyderivable from the above-mentioned parameters, for example, losstangent, expressed as tan

${\sigma = \frac{ɛ_{r}^{''}}{ɛ_{r}^{\prime}}},$complex refractive index, expressed as n=√{square root over (ε_(r))},and impedance, expressed as

${Z(\omega)} = {\sqrt{\frac{i\;\omega}{\sigma + {i\;{\omega ɛ}_{r}}}}\mspace{11mu}{( {{{with}\mspace{14mu} i} = \sqrt{- 1}} ).}}$

Herein, a value of a dielectric parameter of a material may be referredto as a dielectric property of the material. For example, having arelative permittivity of about 100000 is a dielectric property of a 0.01M KCl solution in water at a frequency of 1 kHz, at about roomtemperature (20°, for example; it should be noted that some dielectricproperties exhibit temperature dependence). Optionally, a dielectricproperty more specifically comprises a measured value of a dielectricparameter. Measured values of dielectric parameters are optionallyprovided relative to the characteristics (bias and/or jitter, forexample) of a particular measurement circuit or system. Values providedby measurements should be understood to comprise dielectric properties,even if influenced by one or more sources of experimental error. Theformulation “value of a dielectric parameter” is optionally used, forexample, when a dielectric parameter is not necessarily associated witha definite material (e.g., it is a parameter that takes on a valuewithin a data structure).

Dielectric properties as a function of frequency have been compiled formany tissues, for example, C. Gabriel and S. Gabriel: Compilation of theDielectric Properties of Body Tissues at RF and Microwave Frequencies(web pages presently maintained atwww(dot)niremf(dot)ifac(dot)cnr(dot)it/docs/DIELECTRIC/home(dot)html).

SUMMARY OF THE INVENTION

There is provided, in accordance with some embodiments of the presentdisclosure, a method of estimating joint effectiveness of a plurality oftissue-ablating operations, the method comprising: receiving dataindicative of parameters of the plurality of tissue-ablating operations,the data including: data indicative of distance between sub-lesionsformed by the tissue-ablating operations, and data indicative of atleast one additional parameter having a value varying among thetissue-ablating operations; and estimating, based on the received dataand using computer circuitry, the joint effectiveness of the pluralityof tissue-ablating operations.

In some embodiments, the estimated joint effectiveness is provided as anindication of the likely effectiveness of the tissue-ablating operationsto prevent recurrence of myocardially-conducted electrical impulsetransmission across the region of tissue extending between a pluralityof sub-lesions formed by the tissue-ablating operations.

In some embodiments, the estimated joint effectiveness is provided as anindication of a need to perform a further tissue-ablating operation toprevent recurrence of myocardially-conducted electrical impulsetransmission across the region of tissue extending between a pluralityof sub-lesions formed by the tissue-ablating operations.

In some embodiments, the at least one additional parameter comprisesrelative time of the occurrence of the tissue-ablating operations.

In some embodiments, the at least one additional parameter is indicativeof a state of edema elicited by an edema-inducing operation performedearlier than the latest of the plurality of tissue-ablating operations.

In some embodiments, the state of edema is estimated based on a timesince an edema-inducing operation.

In some embodiments, the edema-inducing operation is one of thetissue-ablating operations.

In some embodiments, the estimating comprises estimating jointeffectiveness the ablation operations are expected to have after theestimating, and at least half an hour after the tissue-ablatingoperations.

In some embodiments, the estimating is for joint effectiveness of thetissue-ablating operations after the estimating, and following a periodof recovery from the tissue-ablating operations.

In some embodiments, estimating is of the joint effectiveness of theablation operations at least one week after the tissue-ablatingoperations and the estimating.

In some embodiments, the received data comprise data indicative ofdielectric measurements of the ablated tissue.

In some embodiments, the dielectric measurements are indicative oftissue thickness at locations of the sub-lesions.

In some embodiments, the dielectric measurements are indicative oftissue ablation in the sub-lesions.

In some embodiments, the received data comprise parameters of operationof an ablation device during the tissue-ablating operations.

In some embodiments, the parameters of operation of the ablation devicecomprise any one or more of the duration and power of energy delivery tothe sub-lesions.

In some embodiments, the parameters of operation of the ablation devicecomprise any one or more of contact force and dynamics of contact of anablation probe of the ablation device with tissue at the sub-lesions.

In some embodiments, the at least one additional parameter comprisesmeasurements of any one or more of temperature and impedance dropmeasured at the ablated tissue.

In some embodiments, the method further comprises indicating theestimated joint effectiveness; wherein the indicating is beforecompletion of planned tissue-ablating operations planned to complete anablation line comprising the sub-lesions.

In some embodiments, the at least one additional parameter comprisesindications of tissue wall thickness at locations of the sub-lesions.

In some embodiments, the locations of the sub-lesions comprise locationsof the cardiac wall.

There is provided, in accordance with some embodiments of the presentdisclosure, a system for estimating joint effectiveness of a pluralityof tissue-ablating operations, the system comprising computer circuitryconfigured to: receive data indicative of parameters of the plurality oftissue-ablating operations, the data including: data indicative ofdistance between sub-lesions formed by the tissue-ablating operations,and data indicative of at least one additional parameter having a valuevarying among the tissue-ablating operations; and estimate, based on thereceived data and using computer circuitry, the joint effectiveness ofthe plurality of tissue-ablating operations.

In some embodiments, the at least one additional parameter comprisesrelative time of the occurrence of the tissue-ablating operations.

In some embodiments, the at least one additional parameter is indicativeof a state of edema elicited by an edema-inducing operation performedearlier than the latest of the plurality of tissue-ablating operations.

In some embodiments, the state of edema is estimated based on a timesince an edema-inducing operation.

In some embodiments, the computer circuitry estimates jointeffectiveness the ablation operations are expected to have after theestimating, and at least half an hour after the tissue-ablatingoperations.

In some embodiments, the received data comprise data indicative ofdielectric measurements of the ablated tissue.

In some embodiments, the received data comprise parameters of operationof an ablation device during the tissue-ablating operations.

In some embodiments, the parameters of operation of the ablation devicecomprise any one or more of the duration and power of energy delivery tothe sub-lesions.

In some embodiments, the parameters of operation of the ablation devicecomprise any one or more of contact force and dynamics of contact of anablation probe of the ablation device with tissue at the sub-lesions.

In some embodiments, the at least one additional parameter comprisesmeasurements of any one or more of temperature and impedance dropmeasured at the ablated tissue.

In some embodiments, the system further comprises a display, and whereinthe computer circuitry is configured to indicate the estimated jointeffectiveness using the display.

In some embodiments, the at least one additional parameter comprisesindications of tissue wall thickness at locations of the sub-lesions.

There is provided, in accordance with some embodiments of the presentdisclosure, a method of estimating joint effectiveness of a plurality oftissue-ablating operations, the method comprising: receiving dataindicative of parameters of the plurality of tissue-ablating operations,the data including data indicative of at least one of: a temporalrelation among the tissue-ablating operations, a spatial relation amongthe tissue-ablating operations, a state of edema at the tissue ablatedby the tissue-ablating operations, parameters of operation of anablation device during the tissue-ablating operations, and temporaldevelopment of impedance drop measured at the ablated tissue; andestimating, based on the received data and using computer circuitry, thejoint effectiveness of the plurality of tissue-ablating operations.

In some embodiments, the estimated joint effectiveness is provided as anindication of a need to perform a further tissue-ablating operation toprevent recurrence of myocardially-conducted electrical impulsetransmission across the region of tissue extending between a pluralityof sub-lesions formed by the tissue-ablating operations.

In some embodiments, the temporal relation comprises relative time ofthe occurrence of the tissue-ablating operations.

In some embodiments, the state of edema is estimated based on a timesince an edema-inducing tissue-ablating operation.

In some embodiments, the estimating comprises estimating jointeffectiveness the ablation operations are expected to have after theestimating, and at least half an hour after the tissue-ablatingoperations.

In some embodiments, the estimating is for joint effectiveness of thetissue-ablating operations after the estimating, and following a periodof recovery from the tissue-ablating operations.

In some embodiments, estimating is of the joint effectiveness of theablation operations at least one week after the tissue-ablatingoperations and the estimating.

In some embodiments, the parameters of operation of the ablation devicecomprise any one or more of the duration and power of energy delivery tosub-lesions formed by the tissue-ablating operations.

In some embodiments, the parameters of operation of the ablation devicecomprise any one or more of contact force and dynamics of contact of anablation probe of the ablation device with tissue at sub-lesionlocations during the tissue-ablating operations.

In some embodiments, the method further comprises indicating theestimated joint effectiveness; wherein the indicating is beforecompletion of planned tissue-ablating operations planned to complete anablation line.

In some embodiments, the method further comprises indicating theestimated joint effectiveness.

There is provided, in accordance with some embodiments of the presentdisclosure, a method of estimating joint effectiveness of a plurality ofsub-lesions, the method comprising: receiving data indicating: at leastone sub-lesion characterizing parameter having different values among aplurality of sub-lesions, and the relative time of the occurrence oftissue-ablating operations to produce the sub-lesions; and estimating,using computer circuitry, the joint effectiveness of the plurality ofsub-lesions at preventing myocardially-conducted electrical impulsetransmission across an ablation segment defined by the sub-lesions,based on the at least one sub-lesion characterizing parameter and therelative time of the occurrence of the tissue-ablating operations.

There is provided, in accordance with some embodiments of the presentdisclosure, a method of estimating joint effectiveness of a plurality ofsub-lesions, the method comprising: receiving data indicating: at leastone sub-lesion characterizing parameter having different values among aplurality of sub-lesions, and the relative distance of the sub-lesions;and estimating, using computer circuitry, the joint effectiveness of theplurality of sub-lesions at preventing myocardially-conducted electricalimpulse transmission across an ablation segment defined by thesub-lesions, based on the at least one sub-lesion characterizingparameter and the relative distance of the sub-lesions.

There is provided, in accordance with some embodiments of the presentdisclosure, a method of estimating joint effectiveness of a plurality ofsub-lesions, the method comprising: receiving data indicating: at leastone tissue-characterizing parameter having different values among aplurality of locations at which a plurality of sub-lesions are to beablated, parameters planned to be used for ablating at the plurality oflocations, and the relative positions of the sub-lesions; andestimating, using computer circuitry, the joint effectiveness of theplurality of sub-lesions at preventing myocardially-conducted electricalimpulse transmission across an ablation segment defined by thesub-lesions, based on the one tissue-characterizing parameter, theparameters planned to be used for ablating, and the positions of thesub-lesions.

There is provided, in accordance with some embodiments of the presentdisclosure, a method of estimating joint effectiveness of a plurality ofsub-lesions, the method comprising: receiving data indicating: at leastone tissue-characterizing parameter having different values among aplurality of locations at which a plurality of sub-lesions are to beablated, parameters planned to be used for ablating at the plurality oflocations, and the relative time of the occurrence of thetissue-ablating operations; and estimating, using computer circuitry,the joint effectiveness of the plurality of sub-lesions at preventingmyocardially-conducted electrical impulse transmission across a regionextending therebetween, based on the at least one tissue-characterizingparameter, the parameters planned to be used for ablating, and therelative time of the occurrence of the tissue-ablating operations.

There is provided, in accordance with some embodiments of the presentdisclosure, a method of estimating effectiveness of ablation to form anablation segment comprising a plurality of sub-lesions, the methodcomprising: receiving ablation segment effectiveness parameters; whereinthe ablation segment effectiveness parameters comprise: at least onesub-lesion characterizing parameter having different values among aplurality of sub-lesions that form the segment; and estimating aneffectiveness of the ablation segment in a tissue; wherein theestimating is performed by computer circuitry of an estimatorconstructed based on observed associations between previously obtainedablation segment effectiveness parameters, and observed ablation segmenteffectiveness, the estimator being applied to the received ablationsegment effectiveness parameters.

In some embodiments, the ablation segment effectiveness parametersfurther comprise relative positions of the sub-lesions.

In some embodiments, the ablation segment effectiveness parametersfurther comprise a relative time between tissue-ablating operations ofthe sub-lesions.

There is provided, in accordance with some embodiments of the presentdisclosure, a method of estimating joint effectiveness of a plurality ofsub-lesions and providing an indication of the estimated jointeffectiveness, the method comprising: receiving dielectric measurementsmeasured at a plurality of sub-lesions; estimating, using computercircuitry, the joint effectiveness of the plurality of sub-lesions atpreventing myocardially-conducted electrical impulse transmission acrossa region extending therebetween, based on the dielectric measurements;and providing the indication of estimated joint effectiveness.

In some embodiments, the receiving includes receiving the relativepositions of the sub-lesions and the estimating is further based on therelative positions of the sub-lesions.

There is provided, in accordance with some embodiments of the presentdisclosure, a method of estimating joint effectiveness of a plurality ofsub-lesions and providing an indication of the estimated jointeffectiveness, the method comprising: receiving data indicatingparameters of a plurality of sub-lesions; estimating, using computercircuitry, the joint effectiveness of the plurality of sub-lesions atpreventing myocardially-conducted electrical impulse transmission acrossa region extending therebetween, based on the received data; andproviding the indication of estimated joint effectiveness.

In some embodiments, the indication is of the likely effectiveness ofthe tissue-ablating operations to prevent recurrence ofmyocardially-conducted electrical impulse transmission across the regionof tissue extending between the plurality of sub-lesions.

In some embodiments, the indication is of a need to perform a furthertissue-ablating operation to prevent recurrence ofmyocardially-conducted electrical impulse transmission across the regionof tissue extending between the plurality of sub-lesions.

In some embodiments, the estimating of joint effectiveness is affectedby a plurality of the parameters in combination, such that for at leastsome combinations of values of those parameters, no single parameter isitself sufficient to yield the value of the estimating.

In some embodiments, the at least some combinations comprises anycombination wherein at least one of the parameters is within apredetermined range of values of the at least one of the parameters.

In some embodiments, the parameters of the plurality of sub-lesionscomprise one or more of: how the sub-lesions are planned to be formed,how the sub-lesions are actually formed by ablating operations, howeffective the sub-lesions are individually, and measurements of thesub-lesions during or after ablation.

Unless otherwise defined, all technical and/or scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which the invention pertains. Although methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of embodiments of the invention, exemplarymethods and/or materials are described below. In case of conflict, thepatent specification, including definitions, will control. In addition,the materials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system”(e.g., a method may be implemented using “computer circuitry”).Furthermore, some embodiments of the present disclosure may take theform of a computer program product embodied in one or more computerreadable medium(s) having computer readable program code embodiedthereon. Implementation of the method and/or system of some embodimentsof the disclosure can involve performing and/or completing selectedtasks manually, automatically, or a combination thereof. Moreover,according to actual instrumentation and equipment of some embodiments ofmethods, systems, and/or computer program products of the presentdisclosure, several selected tasks could be implemented by hardware, bysoftware or by firmware and/or by a combination thereof, e.g., using anoperating system.

For example, hardware for performing selected tasks according to someembodiments of the present disclosure could be implemented as a chip ora circuit. As software, selected tasks according to some embodiments ofthe present disclosure could be implemented as a plurality of softwareinstructions being executed by a computer using any suitable operatingsystem. In an exemplary embodiment, one or more tasks according to someexemplary embodiments of method and/or system as described herein areperformed by a data processor, such as a computing platform forexecuting a plurality of instructions. Optionally, the data processorincludes a volatile memory for storing instructions and/or data and/or anon-volatile storage, for example, a magnetic hard-disk and/or removablemedia, for storing instructions and/or data. Optionally, a networkconnection is provided as well. A display and/or a user input devicesuch as a keyboard or mouse are optionally provided as well. Any ofthese implementations are referred to herein more generally as instancesof computer circuitry.

Any combination of one or more computer readable medium(s) may beutilized for some embodiments. The computer readable medium may be acomputer readable signal medium or a computer readable storage medium. Acomputer readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium and/or data usedthereby may be transmitted using any appropriate medium, including butnot limited to wireless, wireline, optical fiber cable, RF, etc., or anysuitable combination of the foregoing.

Computer program code for carrying out operations for some embodimentsof the present disclosure may be written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Java, Smalltalk, C++ or the like and conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages. The program code may execute entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Some embodiments of the present disclosure may be described below withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products. It will be understoodthat each block of the flowchart illustrations and/or block diagrams,and combinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer program instructions. Thesecomputer program instructions may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Some embodiments of the present disclosure are described, by way ofexample only, with reference to the accompanying drawings. With specificreference now to the drawings in detail, it is stressed that theparticulars shown are by way of example, and for purposes ofillustrative discussion. In this regard, the description taken with thedrawings makes apparent to those skilled in the art how embodiments ofthe present disclosure may be practiced.

In the drawings:

FIG. 1A is a block diagram of components of a system for ablation and/ortracking the position of an intra-body catheter, which may be used withan estimator, in accordance with some embodiments of the presentdisclosure;

FIG. 1B is a schematic flowchart of a method of deriving and applying anestimator for predicting lesion effectiveness, according to someembodiments of the present disclosure;

FIGS. 2A-2C schematically illustrate aspects of lesioning to block oftissue conduction, for example for the treatment of atrial fibrillation,in accordance with some exemplary embodiments of the disclosure;

FIG. 3A is a schematic illustration of a tissue wall of a left atrium,including roots of left and right pulmonary veins, and a preliminaryline of planned ablation, in accordance with some exemplary embodimentsof the present disclosure;

FIG. 3B is a schematic illustration of tissue wall, together with asection of a phrenic nerve, an esophagus, and roots of pulmonary veins,each of which is potentially vulnerable to lesion damage due toproximity with tissue wall, in accordance with some exemplaryembodiments of the present disclosure;

FIG. 3C is a schematic illustration of an alternative line of plannedablation, in accordance with some exemplary embodiments of the presentdisclosure;

FIG. 3D is a schematic illustration highlighting details of a plannedset of sub-lesions of a lesion line, and their traversal along a line ofplanned ablation, in accordance with some exemplary embodiments of thepresent disclosure;

FIG. 3E is a schematic illustration of a planned set of ablationsub-lesions for ablating along line of planned ablation, in accordancewith some exemplary embodiments of the present disclosure;

FIG. 4A schematically represents a range of options for prior inputs toa lesion effectiveness estimator and/or for machine learning of theestimator, comprising parameters potentially relating to lesioneffectiveness, according to some embodiments of the present disclosure;

FIG. 4B schematically represents a range of options for prior inputs toa lesion effectiveness estimator and for machine learning of theestimator, comprising parameters potentially relating to lesioneffectiveness, including prior inputs defining conditions for lesioneffectiveness, according to some embodiments of the present disclosure;

FIGS. 5A-5B schematically illustrate aspects of the planned placement ofsub-lesions of a lesion line related to myocardial fiber direction, inaccordance with some exemplary embodiments of the present disclosure;

FIG. 6 is a schematic flowchart of a method of deriving and applying anestimator for predicting ablation line effectiveness, according to someembodiments of the present disclosure;

FIGS. 7A-7B schematically illustrate adjacency effects of tissue lesionsmade in two different sequences, in accordance with some exemplaryembodiments of the present disclosure;

FIGS. 8A-8C illustrate the 3-D display of a lesion plan for a leftatrium, in accordance with some exemplary embodiments of the presentdisclosure;

FIG. 9 is a schematic flowchart of a method of chaining estimatorspredicting lesion effectiveness and ablation line effectiveness into asequence, according to some embodiments of the present disclosure;

FIG. 10A schematically represents options for prior inputs to anablation line effectiveness estimator and for machine learning of theestimator, comprising parameters potentially relating to ablation lineeffectiveness, including inputs comprising output of a lesioneffectiveness estimator, according to some embodiments of the presentdisclosure;

FIG. 10B schematically represents options for prior inputs to anablation line effectiveness estimator and for machine learning of theestimator, comprising parameters potentially relating to ablation lineeffectiveness for use with a stand-alone ablation line effectivenessestimator according to some embodiments of the present disclosure;

FIG. 11 schematically represents different periods for acquiring priorinputs to estimators, and their relationship to different periods foracquiring feedback inputs to estimators, according to some embodimentsof the present disclosure;

FIG. 12 schematically illustrates a method of real-time use of anablation plan with optional adjustment, in accordance with someexemplary embodiments of the present disclosure;

FIG. 13A illustrates the 3-D display of a planned lesion ablation linefor a left atrium, along with an ablation probe, in accordance with someexemplary embodiments of the present disclosure;

FIG. 13B illustrates an interior-D view of left atrium, probe, andplanned ablation line, in accordance with some exemplary embodiments ofthe present disclosure;

FIG. 14A is a schematic flowchart of a method of deriving and applyingan estimator for predicting ablation segment effectiveness, according tosome embodiments of the present disclosure;

FIG. 14B schematically represents a range of options for prior inputs toan ablation segment effectiveness estimator and/or for machine learningof the estimator, comprising parameters potentially relating to ablationsegment effectiveness, according to some embodiments of the presentdisclosure;

FIG. 15A schematically represents a visualization of results of anablation segment effectiveness estimator applied to a plurality ofablation segment used in forming ablation lines within a left atrium,according to some embodiments of the present disclosure;

FIG. 15B schematically represents a visualization of results of anablation segment effectiveness estimator applied to a plurality ofablation segment used in forming ablation lines within a left atrium,according to some embodiments of the present disclosure;

FIG. 16 schematically represents pairwise real-time lesion assessmentbased on use of an ablation segment effectiveness estimator, accordingto some embodiments of the present disclosure;

FIGS. 17A-17D schematically represent indicating changes to the displayof a rendered tissue region due to predicted and/or measured edema,according to some embodiments of the present disclosure; and

FIG. 18 is a schematic flowchart of a method of deriving and applying anelicited edema estimator for predicting edema, according to someembodiments of the present disclosure.

DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION

The present invention, in some embodiments thereof, relates to systemsand methods for treatment with intrabody catheters and, moreparticularly, but not exclusively, to systems and methods for estimatingprognosis of ablation treatment effectiveness, and/or planning and/ordynamically adjusting planning of treatments such as ablation treatmentsperformed using intrabody catheters based on the estimation.

Overview

An aspect of some embodiments of the current invention relates to theuse of estimators to predict short, medium, and/or long-term outcomes ofan ablation procedure, or portion thereof (ablation treatmenteffectiveness). In some embodiments, the ablation procedure is aprocedure to ablation atrial wall tissue, e.g. to treat cardiacarrhythmias, such as: atrial fibrillation. Optionally, the ablationprocedure is carried out using an RF ablation probe. The term “ablationtreatment effectiveness” may include lesion effectiveness (also referredto as effectiveness of an ablation lesion), ablation line effectiveness(also referred to as effectiveness of an ablation line), and/or ablationsegment effectiveness as next described. As used herein, an estimator oreffectiveness estimator may refer to a lesion effectiveness estimator,ablation line effectiveness estimator and/or ablation segmenteffectiveness estimator (also referred as segment estimator or ablationsegment estimator).

In some embodiments of the invention, an estimator may be used topredict acute (e.g., immediate) and/or persistent (e.g., over a periodof at least 1 month, 3 months, 6 months, 1 year, 2 years, 5 years, oranother longer, shorter, or intermediate period) effectiveness of anablation treatment.

In some embodiments of the invention, a lesion effectiveness estimatormay be used to predict acute (e.g., immediate) and/or persistent (e.g.,over a period of at least 1 month, 3 months, 6 months, 1 year, 2 years,5 years, or another longer, shorter, or intermediate period)effectiveness of an ablation lesion. Effectiveness of an ablationlesion, in some embodiments, comprises completeness of electricalisolation across, under, or over the lesion (transmurality of thelesion).

In some embodiments of the invention, an ablation line effectivenessestimator may be used to predict acute (e.g., immediate) and/orpersistent (e.g., over a period of at least 1 month, 3 months, 6 months,1 year, 2 years, 5 years, or another longer, shorter, or intermediateperiod) effectiveness of an ablation line lesion. Effectiveness of anablation line, in some embodiments, comprises completeness of electricalisolation across, under, or over the ablation line (transmurality of theablation line).

“Effectiveness” is a general term used herein to refer to achievement ofany targeted outcome, for example, electrical isolation, absence ofdisease (optionally, absence of disease for some particular periodpost-treatment), and/or safety conditions, according, for example, tothe feedback data used in the construction of the estimator. Any suchtargeted outcome is also referred to herein as a “criterion ofeffectiveness”.

An aspect of some embodiments of the current invention relates to theuse of estimators to plan and/or predict outcomes of an ablationprocedure; and more particularly, in some embodiments, to plan and/orpredict outcomes related to ablation treatment effectiveness of anablation segment (herein, ablation segment is a portion of an ablationline comprising the positions of a plurality of sub-ablations andregions therebetween; clarifications to this definition are provided inthe section entitled Estimators for Ablation Segment Effectivenessherein) ablated as part of an ablation procedure.

Use of a rule of thumb comprising a static recommendation for sub-lesiondistances and sub-lesion ablation parameters raises potentiallyincreased risk over-ablating (resulting, e.g., in a slower procedureand/or elevated risk of complication), under-ablating (resulting, e.g.,in an ineffective procedure)—or even both, since different tissueregions may be differently affected even if treated with the samelesioning technique. In some embodiments of the present invention,ablation segment estimators are used which adjust, explicitly orimplicitly, for differences in ablation response among different regionsof tissue; and/or for regions which are changing over time, for example,in response to events (e.g., ablation itself) which tend to elicitedema. In some embodiments of the present invention, such differencesare expressed as parameter values which vary among (are different for)different tissue-ablating operations, sub-lesion positions, and/orablating times.

In some embodiments, an estimator comprises a set of learned weightsapplied, for example, to a neural network, to terms of a model equation,and/or inputs to a function (e.g., an analytical function). In someembodiments, an estimator is nonlinear, e.g., the weights includenonlinear combinations. In some embodiments, the weights and/or one ormore variable of a function (e.g., an analytical function) may becalculated by machine-learning techniques.

Application of an estimator may comprise plugging into the estimatorappropriate input values, and calculating the result. The result may beproduced as an estimated lesion “effectiveness” or an estimated ablationline “effectiveness” or an estimated ablation segment “effectiveness”,wherein effectiveness can be defined and/or expressed differently indifferent embodiments, as described herein. The estimator may be createdbased on a dataset associating input parameters, for example: parametersof the lesion, lesion of the ablation line, and/or lesion of theablation segment; lesioning procedure, and/or patient data parameterswith observed outcomes. Patient data parameters are also referred toherein as patient parameter data.

Optionally, the estimator is created using machine learning to discovercorrelations between input parameters and observed outcomes;additionally or alternatively, the estimator is created, for example,based on general statistical methods. The estimator may be applied toone or more input parameters of subsequent lesions/lesioning procedures,and the result may be used as a basis for further decision making. Forexample, within a procedure, updated estimator results are optionallyused to indicate whether or not the procedure so far is likely to leadto a successful result, or whether mitigating actions should be taken.Optionally, simulated results of intended mitigating actions are alsosubjected to the estimator before being undertaken, for example, to helpselect from among a plurality of mitigating action choices which is mostlikely to be effective.

In some embodiments, an ablation segment estimator may estimate ablationsegment effectiveness (e.g., joint effectiveness of sub-lesions) basedon data indicative of parameters of a plurality of tissue-ablatingoperations. Data indicative of parameters of a plurality oftissue-ablating operations may include data on individual lesionparameter and/or data on lesion interaction parameter. In someembodiments, data indicative of parameters of a plurality oftissue-ablating operations may include a temporal relation among thetissue-ablating operations (e.g., the relative time of the occurrence oftissue-ablating operations to produce the sub-lesions), a spatialrelation among the tissue-ablating operations (e.g., the relativedistance or position of the sub-lesions), a state of edema at the tissueablated by the tissue-ablating operations, sub-lesion characterizingparameter, parameters of operation of an ablation device during thetissue-ablating operations, and temporal development of impedance dropmeasured at the ablated tissue.

In some embodiments, the estimator is implemented as an analyticalfunction, for example a function which may be expressed in a form (e.g.,for a two sub-lesion segment) such as E=f(A₁,A₂,I_(1,2)), wherein eachA_(x) represents parameters indicating the individually consideredformation and/or characteristics of some sub-lesion x (also referredherein as individual lesion parameter), and I_(x,y) representsparameters indicative of interactions between two individual sub-lesionsx and y (I_(x,y) may also be referred as lesion interaction parameter orsub-lesion interaction parameter). A function comprising two sub-lesionsis described for the sake of explanation. Optionally, more than twointeracting sub-lesions may be accounted for in the ablation segment;for example, E=f(A₁, . . . A_(n),I_(1,2), . . . I_(1,n) . . .I_(n-1,n)). It should be understood that interaction terms representinginteractions among multiple sub-lesions are optionally included in someembodiments of the invention (e.g., I_(x,y,z, . . .) ).

Optionally, there may be no particular distinction in the estimatoritself made between lesion interaction parameters I_(x,y) and individuallesion parameters A_(x); however the conceptual distinction is usefulfor purposes of explanation. Descriptions of these two parameter typeswhich follow should be understood to optionally apply to parameters ofany ablation segment estimator, whether implemented as an analyticalfunction, machine learning result, statistical association, or anotherimplementation.

Parameters in A_(x), in some embodiments, comprise parametersrepresenting how the sub-lesion is planned to be formed (e.g., whereand/or with what ablation device settings), parameters of how thesub-lesion is actually formed (e.g., where, with what ablation devicesettings, and/or with what quality of contact), and/or measurements ofthe sub-lesion at one or more times during and/or after its formation(e.g., measurement of a change in impedance as a result of lesioning;optionally a change which is exponential and/or otherwise characteristicof ablation outcome in its behavior over time). In some embodiments,local tissue conditions such as tissue thickness and/or thermalproperties are also described in parameters of A_(x). In the case ofsub-lesions formed by dragging an ablation probe across tissue, motionof the ablation probe and/or contact quality of the ablation probe withtissue as a function of position and/or time are optionally representedin one or more parameters of A_(x).

Sub-lesion interaction parameters represented in I_(x,y), in someembodiments, comprise parameters representing, for example, distancesbetween and/or other spatial relations among (e.g., locations relativeto tissue landmarks such as ridges, fossae, and/or vein roots)sub-lesions, relative time of the creation of sub-lesions (and/oranother temporal relation such as time of creation since anedema-inducing event), structural features of tissue (e.g., orientationof myocardial fibers) in a region between sub-lesions, and/or featuresof tissue affecting lesioning dynamics (e.g., tissue type, tissuethickness, and/or local conditions affecting energy transfer and/ordissipation). An estimator taking into account effects of lesioninteraction parameters I_(x,y), as well as effects of each of aplurality of sub-lesions individually may be understood as estimatingjoint effectiveness of those sub-lesions to form an ablation segment(effectively, their joint effectiveness is the segment effectiveness),and/or the joint effectiveness of tissue-ablation operations (planned oractual) to form an effective ablation segment comprising thosesub-lesions. Two or more sub-lesions, between which effectiveness ofablation is particularly to be estimated, together form a lesionsegment. To the extent that sub-lesions form an effective lesionsegment, they are also said herein to possess joint effectiveness.Conceptually, joint effectiveness of a plurality of lesions may beunderstood to arise in part from how their individual forms and/orprocesses of formation interact to form an ablation segment; interactionin turn indicated by lesion interaction parameters I_(x,y), in someembodiments.

In some embodiments, the ablation segment effectiveness estimator(however implemented) may also accept patient data parameters. Patientdata parameters may include, for example: age, sex, weight, bloodpressure, patient medical history, laboratory tests or self-tests,and/or patient genetics.

In some embodiments, an effectiveness estimator is used to predictlong-term persistence of lesions. Optionally, planning of follow-uptreatment is based at least partially on predictions of theeffectiveness estimator.

In some embodiments, an effectiveness estimator may take into accountthe occurrence (or predicted occurrence) of edema in the vicinity of alesion location. Edema is typically acutely elicited in heart tissue bylesioning or mechanical insult (that is, such operations areedema-inducing and may be referred to herein as “edema-inducingoperation”), and once developed (after several minutes) can interferewith the effect of subsequent ablations. In some embodiments, anestimator takes into account the positions and relative times of recentablations in order to help predict the effectiveness of subsequentablations (actual ablations and/or planned ablations). Optionally, thesystem suggests alternative plans to help adjust for the effects ofedema—for example, suggesting a different order of ablations, differenttiming, and/or different operating settings (power, time, frequency,phase) for the ablation modality.

In some embodiments of the invention, an ablation line effectivenessestimator is used to predict acute and/or persistent effectiveness of anablation line for overall completeness of electrical isolation (e.g., toestimate a likelihood of there being or developing an inter-lesion gapallowing electrical reconnection). An ablation line, in someembodiments, comprises a group of lesions introduced together byablation to achieve a clinical result. Typically, the lesions areintroduced as a chain of lesions extending along a pathway (herein, thismay be referred as an ablation line, ablation path, lesion line, lesionpath, or path), with the target of creating an uninterrupted barrier toelectrical transmission from one side to the other. Even if eachindividual lesion of an ablation line is apparently transmural andpermanent (for example, as actually measured, and/or as judged and/orpredicted by the lesion effectiveness estimator), there remains apossibility that the individual lesions are placed so that the ablationline overall still allows electrical impulse transmission (that is,myocardially-conducted electrical impulse transmission) across it. Theablation line effectiveness estimator, in some embodiments, is appliedto data describing the overall shape of the ablation line, from which anestimate of the ablation line's overall electrical isolationeffectiveness in the immediate, short-term, and/or long-term future isproduced. In some embodiments, this may applied to an ablation segmenteffectiveness estimator, that is even if each individual lesion of anablation segment is apparently transmural and permanent, there remains apossibility that the individual lesions are placed so that the ablationsegment overall still allows electrical impulse transmission (that is,myocardially-conducted electrical impulse transmission) across it.

The ablation line effectiveness estimator is optionally created based ona dataset associating input parameters of the ablation line and/orablation line procedure with observed outcomes. The estimator may beapplied to the input parameters of subsequent ablation procedures, andthe result optionally used as a basis for further decision making. Forexample, within a procedure, updated estimator results are optionallyused to indicate whether or not the procedure so far is likely to leadto a successful result, or whether mitigating actions should be taken.Optionally, simulated results of intended mitigating actions are alsosubjected to the estimator before being undertaken, for example, to helpselect from among a plurality of mitigating action choices which is mostlikely to be effective.

An aspect of some embodiments of the current invention relates tosystems and methods for planning catheter ablation (using a probe of thecatheter) of a tissue in a patient (producing what is referred to hereinas an ablation plan, lesion plan, line of planned ablation, lesioningplan, plan of ablation treatment, or planned lesion). Planned actions ofthe ablation plan, and/or performances of actions to ablate during anablation procedure (optionally according to such a plan) are alsoreferred to herein as “tissue-ablating operations”.

In some embodiments, an ablation plan includes specification of whereablation is to occur; optionally defined as a line or path, an area,and/or a volume (herein, ablation plans for a path are provided asexamples, without limitation away from embodiments using areal orvolumetric specifications). An ablation plan optionally comprises thedefinition of ablation parameters along the ablation line (for example,frequency, total energy delivered, power and/or timing). An ablationplan optionally specifies movements of an ablation probe moreparticularly—for example, from what start point, in what order, to whatend point, at what angle, and/or with what timing between movements.Optionally, the ablation plan includes specification of the ablationcatheter (e.g., its probe) itself.

In some embodiments, the method for planning an ablation line comprisesreceiving (for example, receiving by a lesion planning system, by aprocessor configured to implement lesion planning, and/or from a user)an indication of a preliminary target form of a lesion to be formed onand/or within a target anatomical structure of the patient by theplanned catheter ablation. Optionally, the preliminary target form isindicated as a path (for example a continuous path, and/or a pathdescribed as a series of locations to be lesioned) specified withrespect to a representation (e.g., a 3-D display) of patient-specificanatomy.

In some embodiments, patient-specific anatomy comprise 3-D imaging data(for example, MRI, CT, NMR, and/or data from another imaging modality)describing and/or displaying patient-specific anatomy. In someembodiments, the data are marked (and/or characterized after receipt)with respect to thermal and/or dielectric properties. Optionally,thermal properties include, for example, thermal conductivity, heatcapacity, rate of active heat transfer (for example, by bloodperfusion), and/or rate of metabolic heat generation. Optionally,dielectric properties include, for example, the frequency-dependentrelative permittivity of tissue, and/or another property related torelative permittivity; for example, as described in the Background ofthe Invention, herein.

In some embodiments, planning an ablation plan is described herein interms of the placement of the ablation path, the plan of ablation probemovement along that path and/or the parameters of ablation whichoptionally are selected to vary as the probe moves along the path. Insome embodiments, these plan features are optionally determined togetherand/or in parallel. For example, which sub-lesion positions are optimalalong a portion of the lesion path is potentially influenced by how muchand/or with what timing lesioning energy is delivered. In someembodiments, determining a final ablation plan comprises iterativelyadjusting these plan features to approach more optimal results, and/orgenerating a selection of alternative plans from which the most optimalresult is chosen. In some embodiments, one or more steps of suchplanning may be carried out by application of a thermal and/ordielectric property simulation of the tissue to be treated.

In some embodiments, planning an ablation plan includes planning anoptimal path. In some embodiments, the optimal path may be understood asthe path which best simultaneously satisfies several, potentiallycontradictory, constraints and/or criteria. In general, the overallablation plan preferably seeks effectiveness of the block whileprotecting against collateral damage, and achieving the greatest speedof lesioning compatible with these two goals. More specifically, theconstraints and/or criteria include, for example:

-   -   minimization of path length;    -   minimization of sub-lesion number;    -   minimization of complexity, required precision, and/or time of        catheter maneuvering;    -   avoidance of collateral damage to non-target tissue;    -   access to the target, dependent, for example, on anatomy shape        and/or catheter mechanics; and/or    -   features of the target anatomy, for example, tissue wall        thickness, existing lesions, and/or fiber direction.

In some embodiments, the method for planning an ablation line comprisescalculating (e.g., by a lesion planning system) simulated results oflesioning, based on the characterization of data describingpatient-specific anatomy. In some embodiments, estimated results oflesioning may be obtained by an effectiveness estimator. Estimatedresults of lesioning may include short-term effects such as heating,collateral effects on nearby tissue, reversible block, and/or edema; aswell as predictions of long-term effects such as the irreversibility ofblock. Estimated results of lesioning obtained by an effectivenessestimator may include ablation treatment effectiveness.

In some embodiments, the simulation is based on thermal and/ordielectric tissue properties specified in the received data. In someembodiments, the simulation comprises simulation of the effects of powerloss density (PLD) in tissue under excitation by a RF field modeledafter a field produced by an ablation probe of an RF ablation catheter.Optionally, when non-RF ablation is performed (such as by substanceinjection, cryoablation and/or irreversible electroporation), anotherequation is used to simulate the initial distribution of ablating energyto (or its ablating removal from) tissue. Additionally or alternatively,simulation of thermal conduction is also performed (for example, basedon the thermal continuity equation). Optionally, simulation comprisesaccounting for interaction between thermal and dielectric properties,for example, changes in dielectric properties during heating as a resultof temperature change potentially subsequently influence heating itselfin turn. Optionally, simulation comprises accounting for interactionbetween ablation and physiological responses, for example, edema arisingpost-ablation potentially affects how later attempts to ablate the sameand/or a nearby region proceeds. Optionally, this is accounted forbetween sequential sub-lesions, and/or as an ablation probe moves alonga line of planned ablation.

In some embodiments, the estimated results of lesioning may be used inplanning a target form of a lesion (e.g., in planning an ablation).Optionally, the planning comprises planning an ablation line (e.g., aline of locations at which ablation is performed to create sub-lesions),along which a lesion is to be formed. Optionally, the ablation plancomprises specification of ablation parameters to be used along theline—for example, particular positions, angles, and/or pressures forcontact between an ablation probe and target tissue; energies used toactivate the probe (optionally including frequency and/or voltage);selection of electrodes (optionally including specification of phasedactivation of electrodes); and/or durations of ablation. Optionallyparameters of ablation (for example, parameters defining energies,details of positioning, and/or durations) are varied at differentpositions along the line (other spatial arrangement) of plannedablation. Optionally, an ablation plan may include the order ofablations, for example, where ends of a looping line of ablation shouldmeet, and/or placement and/or timing of sub-lesions to take advantage ofpreviously existing lesions and/or recent administration of ablationenergy. Herein, the term “sub-lesion” is used to indicate portions of alarger ablation result created by an ablation probe upon or along aportion of larger area to be lesioned (e.g., defined as a line or path,but not excluding definitions as areas or volumes). Ablation isoptionally performed by a probe moved stepwise between lesion foci (withablation at each step defining a sub-lesion), and/or by dragging anablation probe over a continuous extent of target tissue (where asub-lesion is defined by the extent of dragged-out ablation, and/oroptionally by a change in parameter, for example, a change in ablationpower, rate of drag, or another parameter). Herein, the term lesion,used as a noun, is generally equivalent to the term “sub-lesion”.

Optionally, the ablation plan takes into account (and is formulated toavoid damaging) the patient-specific positions of anatomical structuressubject to collateral damage (for example, the esophagus, phrenic nerve,and/or venous roots, as in the case of ablations to treat atrialfibrillation). Optionally, the ablation plan takes into account aspectsrelated to maneuvering within the confines of an anatomical space. Forexample, there may be mechanical limitations on the maneuvering of anablation catheter. In another example, a requirement for precision ofplacement may be relaxed in some positions along a line of plannedablation (e.g., a tolerance to gaps in a lesion line may be greaterwhere fibers are oriented so that they are cut by, rather than runningbetween, adjacent sub-lesions); while certain maneuvers (such as joininglesion line ends) are potentially more prone to error and/orcomplication. In some embodiments, an ablation plan is designed to matchmore difficult maneuvers to lesion positions where delay and/or error ispotentially less damaging to the end result. In some embodiments, theablation plan is calculated for a shortest line of planned ablation, aminimal number of sub-lesions placed, and/or a minimal use of ablationenergy, compatible with the relative importance (e.g., priority and/orweighting) of other criteria and/or constraints.

In some embodiments, the method comprises providing (for example, to alesion planning system) an indication of the planned target form.Optionally, the indication comprises showing a line of planned ablationtogether with a 3-D representation of the target anatomical structure.Optionally, the indication also comprises display of targeted ablationpositions along the line, and/or the order and/or timing in which theablation positions are to be targeted. The indication may also includedetailed aspects of the plan such as planned lesion size and/orlesioning parameters such as power and duration. Optionally, expectedresults of an ablation plan are presented to a user a priori, forexample, as an ablation line indication presented together with a 3-Dmodel of the target tissue, as one or more estimates of time of ablation(partial or overall), as a likelihood of successful treatment, etc. Insome embodiments, likelihood of successful treatment is calculated basedon success in other patients having similar ablation procedurecharacteristics.

In some embodiments, the planning comprises automatic adjustment of thepreliminary target form of the lesion to satisfy one or more criteriaand/or constraints; for example, criteria and/or constraints affectingsafety, procedure outcome, efficiency of power application, and/ortreatment duration; and/or practicability of the lesion plan. In someembodiments, adjusting a preliminary target form of a lesion may bebased on results obtained by an effectiveness estimator during anablation treatment.

In some embodiments, an ablation plan includes the definition ofintermediate target results which can be monitored while the plan iscarried out. For example, lesion effectiveness or ablation lineeffectiveness may be monitored (e.g., by an estimator) during anablation in progress (for example, based on dielectric property and/orthermal measurements). In some embodiments, intermediate target resultsmay be used to adjust one or more parameters of the ablation inprogress, and/or another parameter of the ablation plan.

In some embodiments, a preliminary target form is provided automaticallyand/or by a user as an indication of a selection of a more generallyspecified lesion form, for example, a selection specified in terms ofone or more anatomical landmarks, and/or a topographic relationship ofthe lesion with respect to the landmarks. For example, the indicationmay be “surrounding a root of a pulmonary vein” (additionally oralternatively, the root of a plurality of veins, of another bloodvessel, or any other relationship between anatomical landmark and lesionform suitable to the application).

An aspect of some embodiments of the current invention relates tosystems and/or methods of dynamic adjustment of an ablation plan of atissue in a patient. In some embodiments, adjustment may be based onresults obtained from an effectiveness estimator. In some embodiments,differences between an ablation plan and the actual ablation as itoccurs are automatically adjusted for by changing the plan in media res,optionally while still taking into account criteria and/or constraintsaffecting safety, procedure outcome, and/or speed and/or practicabilityof the lesion plan. Ablation plan adjustments may occur entirelyautomatically, and/or be provided as suggestions and/or alternatives fora user to follow and/or select among. Optionally, alternatives(particularly when they are presented in response in the contingency ofa complication or error in the procedure) are presented with anindication of likely relative risks/benefits. In some embodiments, aneffectiveness estimator may be used to estimate the relativerisks/benefits.

In some embodiments, the ablation plan includes a sequence of positiontargets describing lesioning positions of a target anatomical structureat which sub-lesions and/or other portions of a completed larger lesionare planned to be created. Optionally, the sequence comprises a discretesequence—for example, a sequence of spot-like sub-lesions along a lineof planned ablation. Optionally, the sequence comprises continuoussequence—for example, a sequence of positions passed through as anablation catheter ablation probe is dragged along a portion of a line ofplanned ablation.

In some embodiments, an ablation plan is received; for example, receivedby a system configured to track a probe of an ablation catheter duringablation. In some embodiments, the sequence of position targets iscompared to the actual (e.g., tracked by a catheter tracking system)positions of an ablation catheter where it performs ablation.Preferably, the comparison occurs during an ablation procedure.Optionally, this is followed by automatic correction (optionallyaugmented by user input such as confirmation and/or selection ofoptions) before certain difficulties caused by delay arise—for example,loss of lock between the relative position of the ablation catheter andthe lesion, and/or evolution of the lesion to a form which may be moredifficult to lesion (tissue typically becomes edematous within a fewminutes of lesioning, which can in turn make it difficult to reliablymake further lesioning adjustments afterward).

In some embodiments, the ablation plan is adjusted, based on differencesbetween the sequence of position targets and the sequence of trackedpositions. Generally, the adjustment seeks to preserve key features ofthe final lesion which are potentially at risk due to a partialdeviation from the plan. One significant form of error which can arisein the treatment of atrial fibrillation by lesioning is the placement ofsub-lesions which are not sufficiently close to prevent impulsetransmission from crossing between them. In some embodiments, the planis adjusts by inserting one or more additional lesions, and/or by addingfurther lesioning energy at one of the sub-lesion positions. At the sametime, in some embodiments, safety constraints are also imposed on theplan: for example, to prevent collateral damage to sensitive structuressuch as the esophagus, venous roots, autonomic ganglia, and/or phrenicnerve.

Before explaining at least one embodiment of the invention in detail, itis to be understood that the invention is not necessarily limited in itsapplication to the details of construction and the arrangement of thecomponents and/or methods set forth in the following description and/orillustrated in the drawings. The invention is capable of otherembodiments or of being practiced or carried out in various ways.

Systems for Ablation and/or Tracking the Position of an Intra-BodyCatheter

Reference is now made to FIG. 1A, which is a block diagram of componentsof a system for ablation and/or tracking the position of an intra-bodycatheter, which may be used with an estimator, in accordance with someembodiments of the present invention.

In some embodiments, the system of FIG. 1A may allow an operator tomonitor progress of an intra-body procedure according to a treatmentplan, for example, lesioning along an ablation, optionally withsufficient accuracy and precision to allow monitoring actual vs. plannedablations. In some embodiments, an estimator may be used to predictacute (e.g., immediate) and/or persistent (e.g., over a period)effectiveness of an ablation treatment.

Optionally, the system is configured to dynamically adjust the ablationplan according to the progress of the procedure and/or based on resultsobtained by an effectiveness estimator during an ablation treatment. Thesystem of FIG. 1A may execute, for example, the method of FIG. 1B and/or6 (either by an estimator embedded in processor 1104 or by a dedicatedestimator 1150. Estimator 1150 may be learned lesion estimator 1604 (asdescribed in FIG. 1B) and/or ablation line estimator 1804 (as describedin FIG. 6 ) and/or ablation segment estimator 2104 (as described in FIG.14A) and/or edema estimator 2604 (as described in FIG. 18 ).

It is noted that the system of FIG. 1A may track the location of thedistal end of the catheter (the probe end) by separately andsubstantially simultaneously tracking the position of sensors,electrodes and/or other conducting ports on the distal end of thecatheter.

As used herein, the terms sensor and electrode are sometimesinterchangeable, for example, where referring to an element thatperforms measurements of one or more electrical properties (e.g.,dielectric properties, conductance, impedance, voltage, current, and/orelectrical field strength). For example, the electrodes may function asthe sensors, such as by transmitting from one electrode to a secondelectrode, where the second electrode functions as a sensor. Impedancemay be measured between respective electrode pairs, and/or between adesignated electrode and a reference electrode (which may be locatedoutside the body and/or within the body, such as on the catheter).

The system of FIG. 1A may provide additional features, for example,estimation of contact force applied by the distal end of the catheter(the probe at the probe end of the catheter) to the tissue wall,estimation of the lesion formation (e.g., estimation of lesionstructure, for example, size, volume and/or depth), estimation of tissuetemperature, and/or mapping of fibrotic regions.

System 1100 may include a program store 1106 storing code, and aprocessor 1104 coupled to program store 1106 for implementing the storedcode. Optionally, more than one processor may be used. It is noted thatprogram store 1106 may be located locally and/or remotely (e.g., at aremote server and/or computing cloud), with code optionally downloadedfrom the remote location to the local location for local execution (orcode may be entirely or partially executed remotely).

System 1100 may include an imaging interface 1110 for communicating withone or more anatomical imaging modalities 1111 that acquire a dataset ofimaging data of a patient, for example, anatomical imaging data, e.g., acomputer tomography (CT) machine, an ultrasound machine (US), a nuclearmagnetic resonance (NM) machine, a single photon emission computedtomography (SPECT) machine, a magnetic resonance imaging (MRI) machine,and/or other structural and/or functional anatomical imaging modalitymachines. Optionally, imaging modality 1111 acquires three dimensional(3-D) data and/or 2-D data. It is noted that the anatomical images maybe derived and/or acquired from functional images, for example, fromfunctional images from an NM machine.

System 1100 may include an output interface 1130 for communicating witha display 1132, for example, a screen or a touch screen. Optionally,physically tracked location coordinates are displayed within apresentation of the dataset; for example, the 3-D acquired anatomicalimages are displayed on display 1132, with a simulation of the locationof the distal end of the catheter within the displayed image.

System 1100 may include an electrode interface 1112 for communicatingwith a plurality of physical electrodes 1114 and/or sensors (optionally,the electrodes serve as the sensors) located on a distal end portion ofa physical catheter 1116 designed for intra-body navigation; forexample: an electrophysiology (EP) ablation catheter, and/or anotherablation catheter (e.g., a chemical ablation or injection catheter).Alternatively or additionally, system 1100 includes a navigationinterface 1134 for communicating with a catheter navigation system 1136;optionally a non-fluoroscopic navigation system; optionally, animpedance measurement based system.

In some embodiments, intra-body navigation is performed based on bodysurface electrodes that receive and/or transmit current (e.g.,alternating current) in different frequencies and/or different timesbetween co-planar directions. Analysis of the electrical and/or thermalparameters obtained from the sensors of the catheter, separated into thedifferent channels, is optionally used to estimate the location of eachsensor relative to each body surface electrode. A calibration of thedistances between the sensors (e.g., based on manufacturingspecifications of the catheter, and/or measurements such as usingfluoroscopy or other methods) may be performed.

Optionally, system 1100 includes a sensor interface 1126 forcommunicating with one or more sensors 1128, which may be in the body orexternal to the body; for example, for measuring electrical and/orthermal parameters, for example, impedance and/or conductivity and/orthermal conductivity and/or heat capacity and/or metabolic heatgeneration of the blood, the myocardium, and/or other tissues.

Optionally, system 1100 includes a data interface 1118, forcommunicating with a data server 1122, directly or over a network 1120,to acquire estimated dielectric and/or thermal tissue values forassociation with the acquired imaging dataset. Alternatively, theestimated dielectric and/or thermal values are stored locally, forexample, on data repository 1108.

In some embodiments, data interface 1118 may be used to acquire patientdata parameters and/or additional data required for estimator 1150.

Optionally, a user interface 1124 is in communication with datainterface 1118, for example, a touch screen, a mouse, a keyboard, and/ora microphone with voice recognition software.

Optionally, system 1100 (e.g., computing unit 1102) includes a connector1140 connecting between catheter 1116 (e.g., RF ablation catheter,injection catheter) and a connector interface 1142 (and/or electrodeinterface 1112). Connector 1140 may be used to add additional featuresto existing catheters, such as off the shelf catheters, for example, RFablation catheters, at least by acting as an input of signalscommunicated by the catheter for processing by system 1100. The signalscommunicated by the catheter are intercepted by circuitry withinconnector 1140 and transmitted to interface 1142 and/or 1112, withoutinterfering with the signal transmission. The intercepted signals may beanalyzed by system 1100, for example, to perform real-time tissuemeasurements (e.g., contact force, pressure, ablated volume and/ordepth, temperature, and/or fibrosis mapping), to perform localization ofthe catheter, to estimate ablation treatment effectiveness and/or toidentify the type of the catheter.

It is noted that one or more of interfaces 1110, 1118, 1112, 1126, 1130,1134, 1142 may be implemented, for example, as a physical interface(e.g., cable interface), and/or as a virtual interface (e.g.,application programming interface). The interfaces may each beimplemented separately, or multiple (e.g., a group or all) interfacesmay be implemented as a single interface.

Processor 1104 may be coupled to one or more of program store 1106, datarepository 1108, and interfaces 1110, 1118, 1112, 1126, 1130, 1134,1142.

Optionally, system 1100 includes a data repository 1108, for example,for storing the dataset (e.g., imaging data of a patient), thesimulation, received electrical and/or thermal parameters, and/or otherdata (such as: patient data parameters). The data may be displayed to auser (e.g., physician) before, during and after the procedure.

It is noted that one or more of processor 1104, program store 1106, datarepository 1108, and interfaces 1110, 1118, 1112, 1126, 1130, 1134, 1142may be implemented as a computing unit 1102, for example, as astand-alone computer, as a hardware card (or chip) implemented within anexisting computer (e.g., catheterization laboratory computer), and/or asa computer program product loaded within the existing computer.

Program store 1106 optionally includes code implementable by processor1104 that represents an effectiveness estimator based on one or morelesion effectiveness parameters and/or other data.

In some embodiments, a dataset of a body portion of a patient includinganatomical imaging data of the patient (optionally 3-D data) isprovided, for example, acquired from imaging modality 1111 (e.g., CT,MRI), retrieved from repository 1108, and/or acquired from an externalserver or other storage. Alternatively or additionally, the dataset isacquired and/or derived from a functional imaging modality, for example,NM and/or SPECT. For example, data from the NM modality may be used toinfer the location of autonomous nervous system components (e.g., one ormore ganglion plexi) designated for treatment on the dataset from the CTmodality. In some embodiments, anatomical imaging data may be otherwiseobtained, for example: anatomical structure may be reconstructed fromone or more signals obtained by electrodes 1114 or sensor 1128. In someembodiments, an intrabody electrode probe comprising electrodes 1114 isused to map out an anatomical space in which a procedure is to beperformed, effectively producing a 3-D model of the space, optionally inparallel with performing the procedure. In some embodiments,reconstruction from the mapping uses known spatial constraints on therelative positions of a plurality of sensing electrodes positioned atknown spaced positions relative to the geometry of the intrabody probe.Optionally, local spatial calibration defined by the spatial constraintsis used in combination with constraints on the spatial coherence ofmeasurements as part of the reconstruction process.

The anatomical imaging data may serve as a basis for geometricalstructure and/or modeling of internal organs of the patient, forexample, the organs are segmented using image segmentation code.

Optionally, the anatomical imaging data includes the target tissue fortreatment in a catheterization procedure; for example, the heart.Optionally, the anatomical imaging data includes tissues surrounding thetarget tissue, for example, a full body scan, a full thorax scan, achest and abdominal scan, and/or a chest scan. For example, for anintra-cardiac ablation procedure, a full thorax scan may be performed.

Optionally, the anatomical imaging data is analyzed and/or processed toidentify different types of tissues within the imaging data, forexample, each pixel data or region is classified into a tissue type.Suitable classification methods include, for example, according to imagesegmentation methods, according to a predefined imaging atlas, and/orbased on Hounsfield units.

It is noted that the patient may undergo imaging before thecatheterization procedure, for example, as a separate outpatientprocedure.

In some embodiments, ablation is carried out according to an ablationplan. Optionally, the ablation plan is specified largely automaticallybased on inputs which set parameters for an ablation procedure such asregions targeted to be isolated by the procedure, optionally along withinformation that characterizes the environment of the procedure—tissuestates and shapes, for example. The ablation plan may be understood ascomprising one or more of the three levels described herein. At the highlevel is the overall line of ablation along which lesions will beformed. In addition to design for effectiveness (that is, to achievecomplete isolation), the line of ablation may be preferably efficient(minimum suitable length), safe (minimized potential for side effects)and/or reliable (avoiding areas that are prone to mistakes and/ordifficulties). At the lowest level are individual lesions (also called“sub-lesions” herein) which should be connected together along the lineof ablation to form a complete block, and moreover should be transmural(extending across the heart wall) so that they also block transmissionthroughout the wall thickness. An intermediate level of planning is atthe level of lesion segments, comprising pairs of adjacent lesions andtheir relationship to one another. Adjacent lesions may interact, e.g.,temporally through residual temperature effects as one lesion is ablatedfollowing another. The lesions also interact with each other spatially,since each lesion segment needs to be placed such that there is noeffective gap permitting transmission between two lesions. Ablationplans may be adjusted, even in the course of a procedure underway, inresponse, for example, to unforeseen conditions and/or events,difficulties in carrying out the original ablation plan, and/orunanticipated results of ablation.

Estimators for Lesion Effectiveness

Reference is now made to FIG. 1B, which is a schematic flowchart of amethod of deriving and applying an estimator for predicting lesioneffectiveness, according to some embodiments of the present disclosure.First, inputs to and operation of machine learning at block 1603 aredescribed (however, it should be understood that creation of anestimator based on associations between input parameters and/ormeasurements and outcomes is optionally performed using a non-machinelearning method, such as general-purpose statistical methods). Thenapplication of a learned lesion estimator 1604 at block 1605 isdescribed. With respect to FIG. 1B, “lesion” refers to an individuallesion focus or “sub-lesion”, formed by operation of an ablation probein contact with tissue, while an “ablation line” refers to a sequence orchain of such lesions.

In some embodiments, ablation treatment of a tissue region such as atissue wall (for example, cardiac tissue of the atria) comprises theformation of a substantially continuous lesion of tissue which serves asa block to conduction. In some embodiments, the targeted region of blockis along a lesion path formed from a plurality of sub-lesions arrangedalong it a substantially contiguous fashion.

The line of planned ablation can be defined on any suitable tissuesurface; for example, within the left atrium (e.g., around one or morepulmonary veins), or within the right atrium (e.g., around one or morebranches of the vena cava). For purposes of discussion, the example ofan ablation line in a left atrium is described, however it is to beunderstood that the discussion also applies, changed as necessary, tothe definition of ablation lines along other surfaces.

Optionally, the user indicates a preferred preliminary line type (e.g.,by selecting from a menu a target such as “around the superior leftpulmonary vein root”, or another such target). Optionally, a preliminaryline of planned ablation is automatically generated and/or selectedbased on the indication.

Effective blockage treatment of an irregular impulse conduction diseasesuch as atrial fibrillation potentially fails when the blockage isbroken or incomplete. However, the procedure for forming a blockinglesion is subject to conflicting requirements, such as the need to avoidcollateral damage to non-target tissue, the difficulty of maneuvering acatheter subject to constrained degrees of freedom, and time pressure tocomplete the procedure as quickly as possible.

Reference is now made to FIGS. 2A-2C, which schematically illustrateaspects of lesioning to block of tissue conduction, for example for thetreatment of atrial fibrillation, according to some exemplaryembodiments of the present disclosure. Shown in FIGS. 2A-2B is a lesionpath 54A which encircles two pulmonary veins 48 of a left atrium (a viewfrom inside the atrium is shown).

In some embodiments, an ablation probe 10 comprising at least oneablation device 103 is moved sequentially along path 54A, ablating at aplurality of locations to create a chain sub-lesions 52 at eachlocation. In some embodiments, ablation device 103 comprises one or moreelectrodes, e.g., an electrode used in RF ablation. Optionally, theelectrode(s) may act as a sensing electrode for sensing dielectricproperties of the tissue near it. Optionally, one or more additionalelectrodes may be provided for sensing dielectric properties.

In FIG. 2B, impulse 955 is shown arising from the vicinity of apulmonary vein 48. Where it encounters a completed sub-lesion 52 (forexample, at boundary 955A), conduction is stopped. However, a gap 52Bpotentially allows impulse portion 57 to escape into surrounding tissue,where it may contribute to an irregular heartbeat. The minimum size of agap allowing conduction can be, for example, about 1.0 mm, 1.3 mm, 1.5mm, or another larger, smaller or intermediate value.

Insofar as lesions may compromise a non-uniform profile through thethickness of a tissue (e.g., as for a hemi-ellipsoid or paraboloid), itshould be understood that any region throughout the tissue thicknessexceeding this gap width (as long as it has sufficient depth, forexample, 0.55 mm, or another value of at least about 0.5 mm-2 mm, tosupport transmission) can serve as a pathway for impulse “escape”. Thus,lesions which superficially contact one another, or even overlap—even iftransmural at some region—may nonetheless (at least in principle) besufficiently distant at some depth to allow impulse escape therebetween.However, for purposes of discussion and illustration, at least partiallytransmural lesions shown herein as superficially contacting aregenerally assumed to be close enough to block transmission therebetweenat any depth, except as otherwise indicated.

The maximum size of the gap which still prevents blockage may alsodepend on the structure of the underlying tissue; for example, adirection of myocardial fibers in relation the orientation of the gap(this is also discussed herein, for example, in relation to FIGS.5A-5B). Optionally, the relevant orientation (which potentially variesthrough the thickness of the tissue) is selected from one or more layersin which the gap may exist.

FIG. 2C illustrates how lesion depth potentially relates to relativelyeffective or ineffective conduction block. Tissue region 50 is shownwith a chain of sub-lesions 52, 52A, 52D, 52C already formed. Thevarious depths of these lesions are schematically outlined as dottedline paraboloids 53, 53A, 53D, 53C.

Electrode 103 is shown in contact with a surface of tissue 50, overlesion and targeted tissue area 52C. Here, the lesion is transmural, tothe degree that it has begun to spread across the opposite surface oftissue 50 at region 53C. Lesion 52A is also a deep lesion, but thedegree of transmurality is lower (for example, a small distance 53A hasbeen left). This may not be a reason for concern, if gap 53A is toosmall to allow impulse conduction. However, at lesion 52D, the lesion istoo shallow, and gap 53D is sufficiently large to allow impulse portion57 to pass through it. In some embodiments, a transmurality gap of about0.55 mm or smaller is considered small enough to prevent impulse escape,depending also in part on the width of the gap.

Although ablation is generally described herein with respect to ablationof an atrial wall for the treatment of atrial fibrillation, it should beunderstood that the descriptions also apply, changed as necessary, tothe planning of ablation in other tissues; for example: neural tissue,tumor tissue (for example, cancer), other abnormal growth tissue such aswarts, skin tissue, mucous membrane, or another tissue.

Reference is now made to FIG. 3A, which is a schematic illustration of atissue wall 50 of a left atrium, including roots of left and rightpulmonary veins 47, 48, and a preliminary line of planned ablation 54,in accordance with some exemplary embodiments of the invention.

Optionally, preliminary line of planned ablation 54 encircles both leftpulmonary veins, for example as shown. Optionally, the line of plannedablation serves as a basis for further modifications which result in afinal ablation line which satisfies certain criteria of safety and/oreffectiveness.

Reference is now made to FIG. 3B, which is schematic illustration oftissue wall 50, together with a section of a phrenic nerve 45, anesophagus 44, and roots of pulmonary veins 47, 48; potentiallyvulnerable to lesion-damage around preliminary line of ablation 54 dueto proximity with tissue wall 50 at regions 61, 62, 63, or 64; inaccordance with some exemplary embodiments of the invention.

In some embodiments, a planned ablation of one tissue (e.g., tissue wall50) involves potential risk of damage to one or more adjoining tissues.For example, thermal ablation (e.g., by RF energy application) whichenters into region 62 of tissue wall 50 potentially also induces heatingin esophagus 44 which could lead to damage. Region 61, adjacent to aportion of phrenic nerve 45, is another region of potential risk;damaging the phrenic nerve can lead to partial respiratory paralysis. Insome embodiments, lesion placement criteria exclude and/or limitlesioning from entering certain regions of the lesioned surface itself.Regions 63 and 64 are defined, for example, to exclude lesions fromentering the ostia of the pulmonary veins.

Exclusion may be defined such that no heating or cooling of an at-risktissue can rise above (or fall below) a certain temperature threshold,e.g., according to a simulated ablation operation. In some embodiments,the temperature threshold is, for example, about 50° C., 55° C., 58° C.,60° C., 65° C., 70° C., 75° C., or another larger, smaller orintermediate temperature. Optionally, a functional criterion is used:for example, a plan which includes heating for more than T seconds atenergy Y by a probe within X mm is preferably excluded. Optionally,values for T, Y and/or X are chosen based on general simulations and/orexperimental data, for use as heuristics in the cases of actual patienttreatments. For example, T is varied between about 15 seconds to about45 seconds; Y varies between about 15 W and 30 W, and/or X variesbetween about 5 mm-10 mm. Use of heuristic criteria has the potentialadvantage of bypassing at least some of the computational load of anindividualized simulation.

In some embodiments, lesioning in a risky area is made potentially saferby controlling parameters such as ablation power and/or ablation timing,optionally in addition to controlling the parameter of ablation probeplacement. Conversely, there may be regions where the risk of collateraldamage is relatively low. For example, relatively low thermalconductivity of adjacent tissue (e.g., air-filled lung tissue)potentially allows more aggressive lesioning of heart wall tissue, whichhas a potential advantage for speed of lesioning.

The line of planned ablation 55 shown in FIG. 3B is minimally disturbedfrom the preliminary line of planned ablation 54 for applying protectivecriteria, without also minimizing path length. However, reference is nowmade to FIG. 3C, which is a schematic illustration of an alternativeline of planned ablation 56, in accordance with some exemplaryembodiments of the invention. In this instance, preliminary line ofplanned ablation 54 is shown aggressively optimized for reduced lengthas alternative line of planned ablation 56. Optionally, preliminary lineof planned ablation 54 is “shrunk” until some sub-lesions producedtherealong would just reach to the limit of vein ostium protectionregions 63, 64. In some embodiments, lengthening the path byover-shrinkage is prevented; the effect is as though a rubber band wereextended between sub-lesions which are not in direct contact with thevein ostium protection regions 63, 64.

In FIG. 3B, line of planned ablation 55 is shown adjusted frompreliminary line of planned ablation 54 by diversions which pull theline centrally away from regions 61 and 62 at which ablation isindicated to accompany a potential risk for collateral damage.

Continuing reference is made to FIG. 3D, and also to FIG. 3E, which is aschematic illustration in a wider anatomical context of the planned setof ablation sub-lesions 220 for ablating along line of planned ablation55, in accordance with some exemplary embodiments of the invention.

It is noted that most of the sub-lesion locations marked (e.g.,sub-lesion 310) are relative large in diameter. Optionally, thisreflects a default ablation setting, wherein a full-power, full-durationablation is planned to be carried out at the given position. This may beappropriate, for example, when the tissue wall 50 is relatively thick,and there is a relatively low risk of serious collateral damage whichallows proceeding rapidly. Somewhat smaller sub-lesion sizes are shown,for example, at sub-lesion 311, and in particular through regions 312,313, and 314. In some embodiments, smaller sub-lesions are formed, forexample, by use of a lower ablation power, and/or by use of a shorterlesioning dwell time. Smaller lesions through region 312, for example,optionally reflect an increase in care to avoid damage to the esophagus(compare, for example, to the positions of ablation sub-lesions 220 inFIG. 3E relative to regions 62 and 63). Optionally, smaller lesionsthrough regions 313 and 314 reflect the presence of another constraintor condition; for example, a thinner tissue wall thickness, and/or alocally higher rate of perfusion which reduces the ability to heatnearby tissue.

Although sub-lesions 220 are drawn as abutting circles, it is to beunderstood that an ablation plan optionally overlaps sub-lesion areas tohelp ensure that deeper tissue is not subject to impulse-transmittinggaps. Optionally, sub-lesion shapes are different than circular, due,for example, to oblique angles of contact between the ablation probe 10and the tissue wall. Optionally, an ablation probe is slowly draggedacross a surface, leaving a more streak-like sub-lesion. Another factorwhich can affect sub-lesion shape is the interaction between heatingdelivered at different sub-lesion locations.

Reference is now made to FIGS. 8A-8C, which illustrate the 3-D displayof a lesion path for a left atrium 700, in accordance with someexemplary embodiments of the invention.

In some embodiments, the lesion path is shown to a user by use of a 3-Ddisplay. FIGS. 8A-8C illustrate one such plan display. A 3-D model of aleft atrium 700 (porcine, in this example) is shown from threeviewpoints. Sub-lesion loci are indicated by the trails of dark marks701 (modeled as embedded spheres, for example). In this case, the lesionlines are shown extending around portions of the roots of the inferiorvena cava 48.

In some embodiments, a goal of ablation is to create a blockage lesionhaving a substantially transmural extent. To meet this goal, thickertissue walls potentially require application of more lesioning energy(e.g., at a higher power and/or for a longer time) than thinner walls.Over-lesioning, however, can weaken the tissue, and/or lead to damage tosurrounding tissue. Over-lesioning can also extend lesioning timeunnecessarily. In some embodiments, tissue thickness throughout theregion targeted for lesioning is characterized, for example based onanalysis of anatomical images of the individual patient (obtained, e.g.,by MRI, CT, or another method), and/or based on dielectric measurements.

Prior Inputs to Machine Learning

In some embodiments, the flowchart block marked 1500 comprises aplurality of “collections” of lesion effectiveness parameters 1500. Eachsuch collection in turn comprises a heterogeneous set of data inputsrelating to a particular lesion (that is, a lesion formed at a focus bya single application of ablation energy; corresponding, for example, tothe sub-lesions 52 of FIGS. 2A-2C). The particular lesion is formed(and/or is planned to be formed) by the operation of an ablationmodality (e.g., radio frequency ablation, cryoablation, microwaveablation, laser ablation, irreversible electroporation, substanceinjection ablation, and high-intensity focused ultrasound ablation)acting on tissue which is targeted for ablation. The data inputs of acollection of lesion effectiveness parameters 1500 may includeindications of conditions of formation of the lesion; for example,lesion placement, ablation tool settings governing lesion formation(such as ablation power, dielectric quality of contact, angle ofcontact, force of contact, and timing of ablation), and/or tissueconditions within which the lesion is situated. Lesion effectivenessparameters 1500 may include lesion measurements such as dielectricmeasurements, calculated results of ablation, and/or other indicationsof the structure of an ablation lesion (e.g., depth, linear size, arealsize, and/or volume of tissue). Optionally, lesion effectivenessparameters 1500 include other data; for example: patient dataparameters, and/or previously acquired ablation data for the samepatient.

As used herein, dielectric measurements may include any measureddielectric parameters, for example: impedance, voltage etc. Dielectricmeasurements may be obtained by one or more electrodes (for example:electrodes 1114 or electrodes 103) and/or by one or more sensors (forexample: sensors 1128). Dielectric measurements may be obtained by oneor more electrodes and/or one or more sensors provided on a probe of theablating catheter (for example: catheter 1116). Dielectric measurementsmay be obtained by one or more sensors provided on a dedicatedintra-body probe, e.g., used solely for such dielectric measurements).Dielectric measurements may be obtained at a single frequency or at aplurality of frequencies. In some embodiments, dielectric measurementsinclude measurement at various frequencies, e.g., from about 10 kHz toabout 1 MHz. In some embodiments, dielectric measurements may includecomplex values. In some embodiments, dielectric measurements may includeimpedance measurements including measurements of impedance betweendifferent electrodes on the ablation catheter (e.g., between a tipelectrode on a probe of the ablation catheter and another electrode onthe same catheter), between one or more electrodes on the ablationcatheter and one or more electrodes on another catheter, and/or betweenone of more of the ablation electrodes and one or more body surfaceelectrodes. In some embodiments, a relevant aspect of impedancemeasurements is taken to be the way in which measured impedance changesover time (its temporal development). As an example of temporaldevelopment: the transition of tissue to a permanently ablated conditionis associated, in some embodiments, with a characteristic time-course ofimpedance change during a period of ablating (that is, during aparticular ablation operation to form a sub-lesion). The time course maybe, for example, exponential (e.g., beginning relatively slowly, thenspeeding up according to an exponent >1); and in particular, thetransition to ablated tissue has been observed to be associated with adecrease in impedance (impedance drop). A potential advantage of usingthe exponential characteristic of the impedance change is to distinguishfrom impedance changes due to non-ablation effects, such as relativemovement of the ablation probe and target tis sue.

Optionally, patient data parameters may include the medical state of thepatient; for example, medications the patient is taking (e.g., which mayaffect the ionic concentration of the tissues of the patient, affectingthe electrical and/or thermal parameters), and a history of previoustreatments (e.g., which may help predict the effects of the currenttreatment). Additional examples of patient data parameters are describedbelow.

Moreover, the inputs of each collection of lesion effectivenessparameters 1500 at least potentially indicate (individually and/or inaggregate) information about the “effectiveness” of the particularlesion to which they relate. In some embodiments of the presentinvention, “effectiveness” is defined in relation to targeted outcomesof cardiac ablation treatments for atrial fibrillation (AF). In AFablation treatments, an effective lesion is a lesion in a cardiac wall(i.e., an atrial wall) which substantially blocks electrophysiologicalimpulse transmission from passing through, over, and/or under it (thatis, an effective lesion provides transmural electrical isolation). Insome embodiments, persistence (or permanence) of the lesion and/or itsblocking characteristics is a criterion of effectiveness. Otherdefinitions of “effectiveness” applicable in some embodiments of thepresent invention are described hereinbelow. In some embodiments,effectiveness relates to conditions of safety. Optionally, for example,“effectiveness” is at least partially defined as comprising avoidance ofsome particular outcome, for example, avoiding lesioning of theesophagus, the phrenic nerve, and/or the venous roots, each of which canpotentially lead to serious complications. Optionally, an estimator isused for any combination of “effectiveness” conditions; additionally oralternatively, a plurality of estimators are used, which optionally eachcover a single criterion of effectiveness, or a plurality ofeffectiveness criteria.

At block 1603, in some embodiments, a plurality of collections of lesioneffectiveness parameters 1500 is provided as a training set data for usein one or more machine learning methods, in order to generate a learnedlesion estimator 1604 (herein, black dots mark the receiving side of aconnecting line). The example of “machine learning” is used herein as anexample of a method of creating an estimator, and should not beconsidered limiting: alternatives include, for example general purposestatistical methods and/or estimator definition based on theoreticalequations accounting for observed correlations. In some embodiments,training set data used in generating the learned lesion estimator 1604are obtained in vivo. In some embodiments, at least part of the trainingset data used to generate the learned lesion estimator are obtained invitro, for example based on lesioning of porcine heart wall.

Feedback Inputs to Machine Learning

In some embodiments of the invention, for each collection of lesioneffectiveness parameters 1500 in the training set data, there is alsoprovided as input to block 1603 a corresponding data collectionindicating observed lesion effectiveness 1602. For example, the observedlesion effectiveness optionally includes a measurement showing one ormore of the following:

-   -   acute electrical isolation is established by the lesion at the        end of the ablation procedure;    -   persistent electrical isolation remains for at least 5 days, 30        days, 60 days, 90 days, or another longer, shorter, or        intermediate duration after the end of the ablation procedure;    -   asymptomatic    -   hospitalization    -   survival    -   AF burden <5%    -   a fibrotic area corresponding to the lesion persists for at        least 5, 30, 60, 90, or another longer, shorter, or intermediate        period after the end of the ablation procedure; and/or    -   the patient is disease free for at least 1 month, 3 months, 6        months, 1 year, 2 years, 5 years, or another longer, shorter or        intermediate period after the end of the ablation procedure.

In some embodiments, lesion effectiveness is a local measure. Thus, themore global conditions just listed (“disease free”, for example) may notapply to such embodiments, since on an individual lesion analysis, itmay not be clear on which (failed) lesion a reappearance of diseaseshould be blamed. There may not even be a single failed lesion as such,only a failure to place otherwise adequate lesions correctly. However,in some embodiments (e.g., FIG. 4B), at least some local context istaken into account, so that if every lesion is estimated as effective,then the overall ablation line should normally be effective as well. Insome embodiments, observed lesion effectiveness relates to safety—forexample that the lesion was or was not associated with a complicationsuch as damage to the esophagus, damage to the phrenic nerve, damage tothe venous roots, and/or a risk-associated event during ablation such ascharring or “steam pop”. Steam pop is a term for a condition whereinrapid expansion of steam during an ablation creates an audible “pop”;this is associated in the literature with a risk of complications suchas heart wall perforation.

In some embodiments, data indicating observed lesion effectiveness maybe obtained by the same catheter that was used to form the ablation. Forexample, a probe of an RF ablation catheter is operated to ablatetissue, and then electrodes of the same catheter are used to measurepotential (e.g., in high frequency electrical fields) induced in thevicinity of the ablation. From these measurements, impedance propertiesindicating lesion state are optionally calculated. In some embodiments,the impedance properties in turn indicate dielectric properties oftissue that are changed as a result of tissue ablation. Dielectricproperties and/or impedance are optionally interpreted as indicatinglocal tissue state and in particular, local tissue state(s) as beingpermanently lesioned (e.g., converted to fibrotic tissue), edematous butnot fibrotic, and/or healthy. Data provided as a collection of observedlesion effectiveness data 1602 is optionally expressed in any or all ofthese formats (voltages, impedances, dielectric parameters, and/ortissue state(s) inferred therefrom). Optionally, another measure ofobserved lesion effectiveness is provided, for example, measurements ofelectrical isolation (e.g., lack of impulse conduction across thelesion), or clinical observation that disease is absent.

Model Input to Machine Learning, and Pre-Processing of Inputs

In some embodiments, the machine learning of block 1603 proceeds on thebasis of an assumed lesion model 1601. Optionally, lesion model 1601 maybe very simple, and simply hypothesizes for the machine learning ofblock 1603 a correlation (naïve as to underlying structure) between theprior inputs of lesion effectiveness parameters 1500 and the feedbackinputs of observed lesion effectiveness 1602. However, it may be apotential advantage to at least partially structure inputs. Thestructure can be provided as part of lesion model 1601, and/or bypre-processing of data provided as inputs. For example, inputs from theside of the lesion effectiveness parameters 1500 are optionallypre-processed to be expressed in terms of lesion size (diameter and/ordepth, for example). Additionally or alternatively, such measurementsare structured by the lesion model itself into indications of lesionsize (e.g., machine learning is applied to a model equation operating onthe lesion effectiveness parameters in one or more combined terms,rather than only in terms each comprising a different raw lesioneffectiveness parameters). Discussion of FIG. 4A describes this andother levels of structuring that are optionally used in some embodimentsof the present invention.

On the feedback side, pre-interpretation of raw measurements indicatinglesion effectiveness (e.g., interpretation of electrical fieldmeasurements such as dielectric measurements to identify local tissuestate) provides a potential advantage by reducing complexity of the dataspace (and potentially noise in the data) before applying machinelearning to it. However, there is a potential disadvantage inover-simplifying the data in observed lesion effectiveness 1602 bypre-interpreting, as this can reduce or destroy correlations latent inthe raw data. Optionally, use of pre-processing enables creatingestimators which are applicable to different types of raw input. Forexample, part of the difference between simulations of lesion size(e.g., before lesioning) and measurements of lesion size after lesioningcan be abstracted away by making lesion size itself one of the inputs inthe lesion effectiveness parameters 1500. That has the potentialadvantage of allowing comparison of predictions from a planned procedurewith predictions from actual procedure results, for example.

In some embodiments, the raw measurements indicating lesioneffectiveness (e.g., electrical field measurements such as dielectricmeasurements) may be used as lesion effectiveness parameters 1500 (e.g.,without any calculations) and may be input as training set data.

Learned Lesion Estimator

After the above-described inputs are suitably defined and received for aplurality of lesions (e.g., 50, 100, 1000, or another larger, smaller,or intermediate number of lesions), machine learning at block 1603 usesof one or more machine learning methods, to produce a learned lesionestimator 1604. Examples of machine learning methods used in someembodiments of the present invention include, for example: decision treelearning, association rule learning, an artificial neural network,deep-learning artificial neural network, inductive logic programming, asupport vector machine, cluster analysis, Bayesian networks,reinforcement learning, representation learning, similarity and metriclearning, and/or another technique taken from the art of machinelearning. In some embodiments, the estimator output (e.g., estimatedlesion effectiveness) may include a binary prediction (e.g., +1 or −1).In some embodiments, the estimator output (e.g., estimated lesioneffectiveness) may include a probabilistic output, e.g., between zeroand one (e.g., a dynamic score). Such embodiments may be advantageouswhen using estimator during an ablation procedure, e.g., to facilitate auser decision whether to proceed or complete the procedure and/or toadjust the ablation plan.

In some embodiments, the learned lesion estimator 1604 comprises a setof learned weights applied to terms of lesion model 1601.

Application of the lesion estimate at block 1605 comprises plugging intothe model appropriate values from other collection of lesioneffectiveness parameters 1500, and calculating the result. The result isproduced as an estimated lesion effectiveness 1606. Other collections ofablation line effectiveness parameters 1500 may refer to ablation lineeffectiveness parameters 1500 collected after learned ablation lineestimator 1604 was created, e.g., ablation line effectiveness parameters1500 collected in run-time during a medical procedure. Other collectionsof ablation line effectiveness parameters 1500 may include dielectricmeasurements measured run-time during a medical procedure. Suchdielectric measurements may be used as inputs to learned lesionestimator 1604 to obtain estimated lesion effectiveness 1606.

It should be noted that learned ablation line estimator 1604 may beupdated and/or adjusted (also referred to as refreshed) based on suchother collections of ablation line effectiveness parameters 1500.Estimated lesion effectiveness 1606 may be presented to a user (e.g.,physician) during a medical procedure. Such Estimated lesioneffectiveness 1606 may be used to adjust an ablation plan.

In some embodiments, the estimated lesion effectiveness 1606 isexpressed in the same terms as used by lesion model 1601 for learning.Optionally, the estimated lesion effectiveness 1606 may also beaccompanied by an estimate of the certainty of the prediction, forexample, based on statistically determined specificity and/orsensitivity. Optionally, post-processing is applied to convert theestimated lesion effectiveness 1606 into another form. For example, thefeedback input in the observed lesion effectiveness 1602 may not itselfencode a spatial extent of the region electrically isolated by aparticular lesion; but rather, for example, it may simply indicate thatthe electrical isolation is sufficient to maintain disease prevention.Since this implies that the lesion is transmural, it is possible,optionally, to infer at a later stage that the depth of the lesion isabout the same as the thickness of the tissue. Similarly, the positionsand sizes of adjoining lesions optionally provide post-processingconstraints on lesion diameter. It should be understood that it is oftenpossible to alternatively make such adjustments in a stage ofpre-processing (e.g., convert “successful block” to size constraints inthe feedback input indicating observed lesion effectiveness 1602),and/or to account for them in lesion model 1601 itself (e.g., definemodel 1601 to output an estimate comparing lesion depth to atrial wallwidth, rather than a binary “successful/unsuccessful” result).

Stand-Alone Lesion Effectiveness Estimator

Reference is now made to FIG. 4A, which schematically represents a rangeof options for prior inputs to a lesion effectiveness estimator and/orfor machine learning of the estimator, comprising parameters potentiallyrelating to lesion effectiveness, according to some embodiments of thepresent disclosure.

The number of possible separate inputs to a collection of lesioneffectiveness parameters, in some embodiments, is both large andoptionally variable. The inputs listed in FIG. 4A may be viewed asproviding a menu of examples of parameters, from which any suitablesubset is optionally used. Optionally, another parameter source notshown among those in the FIG. 4A may be used.

The broadly hierarchical (mostly branched, but in some places looping)arrangement by which optional inputs are shown in FIG. 4A is primarilyused as a guide to help organize the descriptions that follow. Terminalnodes (terms without borders) represent numerous types of basic inputs(e.g. measurements, settings, and outside data such as anatomical atlasdata). Nodes surrounded by thin cloud-shaped borders list intermediateorganizational concepts that link the basic inputs (and/or otherorganizational concepts) as alternative and/or complementarycontributions to the full collection of lesion effectiveness parameters1500, as detailed for each. Optionally (as indicated in the moredetailed descriptions following), some of the cloud-bordered nodesrepresent higher level abstractions used in the lesion effectivenessparameters in place of one or more basic inputs.

Next to the triangle that indicates the root of the hierarchy of lesioneffectiveness parameters 1500 are two clouds with thicker borders. Theseclouds define a basic conceptual division of the lesion effectivenessparameters into two: parameters indicating information about size and/orposition of the lesion itself (parameterized lesion 1530, which may besaid to be characterized by lesion parameterizing data; that is,parameters of the parameterizing data are sub-lesion characterizingparameters), and parameters indicating information about the tissueenvironment in which the lesion is placed (tissue condition 1520).

Apart from the organization just described, the parameters of FIG. 4Abelong to one or more of three time stages:

-   -   Pre-lesioning Inputs indicate, for example, results of planning        activities (ablation line definition, pre-ablation simulations        of ablation extent, etc.)    -   Intra-Lesioning Inputs include, for example, measurements taken        and operational parameters used during a lesioning procedure        (including parameters optionally measured immediately after        lesioning but before the overall procedure ends; for example:        dielectric measurements)    -   Post-Lesioning Inputs include, for example, clinical        observations in the period after the procedure ends (day to        years), post-procedure imaging, and/or follow-up catheterization        procedures.

For some parameters, stages are explicitly mentioned in FIG. 4A (e.g.,as pre-lesioning simulation 1540, intra-lesioning parameters 1561indicative of the conditions of formation of a lesion, andpost-lesioning observations 1532). Staging relevant to other inputs ismentioned where those inputs are described. These three stages ofparameter availability are also discussed with respect to FIG. 11 , inthe context of their relationship to each other, and to different futuretimes for estimation. Starting now from the node labeled parameterizedlesion 1530: In some embodiments, the lesion is parameterized based onone or more of pre-lesioning simulations 1540, post-lesioningobservations 1532, inferred ablation results 1560, and/or any of theother parameters which are shown feeding into these nodes. Inferredablation results 1560 may include one or more inferences of a tissuestate from dielectric measurements and/or any manipulation (such asmathematical manipulation) on such dielectric measurements). Inferredablation results 1560 may include one or more inferred results inferredfrom dielectric measurements by analytical calculations and/or machinelearning techniques, for example, type of tissue (healthy or fibrotic,for example), size of a lesion, thickness of tissue, and/or quality ofcontact with tissue.

In some embodiments, the lesion is parameterized non-geometrically inthe collection of lesion effectiveness parameters 1500 supplied to anestimator. For example, a dielectric measurement 1533 optionallyprovides information about how much local tissue in the region of ameasurement electrode has been lesioned, without necessarily providinginformation about how the lesion is shaped. Potentially, anon-geometrical parameterization may still provide enough informationfor machine learning to converge on an estimator linking the lesionparameterization to an estimated lesion effectiveness.

Additionally or alternatively, in some embodiments, the lesion isparameterized as a geometrical object having a well-defined shape. Forexample, a geometrical parameterization of lesion shape optionallydefines at least a lesion depth and/or a proximal diameter (diameter ofa lesion at the side directly in contact with the lesioning device, suchas a probe of an intra-body catheter). Optionally, these parameters areincorporated into a definition of a geometrical solid representing theshape of the lesion; for example, a frustum of a paraboloid, ellipsoid,cone, or another shape. Parameterization of lesions as geometricalshapes, though potentially subject to estimation errors of its own, hasthe potential advantage of being interconvertible between outputs ofpre-lesioning simulation 1540, inferred ablation results 1560 (and/orintra-lesioning parameters 1561), and post-lesioning observations 1532.Optionally, this may allow the same estimator to be used on data fromdifferent original sources. Additionally or alternatively, a non-spatial“common parameterization” may be used; for example, a spatial definitionof a lesion determined by simulation is optionally re-parameterized bythe results of simulated “measurements”. Optionally, re-parameterizationis be used to convert lesion effectiveness parameters from in vitrostudies into lesion effectiveness parameters more convenient for directin vivo use, and/or conversely.

Each of the three main lesion parametrizing data stages is now discussedin turn.

In some embodiments, pre-lesioning simulations 1540 produceparameterized descriptions of lesions which are planned to be madeduring an ablation procedure. Pre-lesioning simulation 1540 maycomprises simulations such as EM (Electro-Magnetic) simulation; e.g., RFabsorption simulation, and/or thermal simulation on a simulated tissueto be treated. The pre-lesioning simulations 1540 may be based on alesioning plan 1541 which may describe where and how ablations are to bemade, and optionally modulated by relevant parameters of tissuecondition 1520 (e.g. tissue thickness 1545, state of edema 1550, tissuetype(s) 1523, fiber orientation 1525, and/or intra-lesioning temperature1555, each further described below). As examples of plan modulation,ablation intensity (power, and/or time of ablation energy deliver) isoptionally increased for a larger tissue thickness, and/or reduced for ahigher than expected intra-lesioning temperature. Optionally, fiberorientation modulates how close together lesions are planned (lesionsplaced adjacent along fibers are potentially less prone to gap formationat increasing distances than lesions where fibers are oriented to passbetween the adjacent lesions). Tissue having developed edema may requiremore energy to successfully ablate, and/or may be preferably avoided inpreference to another ablation location and/or strategy. Tissue of atype which is already fibrotic may not require additional ablation.

Optionally, the pre-lesioning simulation 1540 is produced as part ofcreating the lesioning plan 1541 in the first place. Insofar as thepre-lesioning simulation optionally comprises spatially definedsimulations such as an RF absorption simulation and/or thermalsimulation, it may be relatively straightforward to define theparameterized lesion 1530 in terms of geometrical shape.

In some embodiments, parameterized lesion 1530 is based on inferredablation results 1560. Similarly as with pre-lesioning simulations 1540,the inferred ablation results may estimate the effects of parametersused during ablation, optionally modulated by the parameters of tissuecondition 1520. However, instead of choosing ablation parameters from alesioning plan 1541, the intra-lesioning parameters 1561 used in actualablation (and, accordingly, also indicative of the conditions offormation of a lesion) are used. Examples of intra-lesioning parameters1561 include ablation probe contact 1565, ablation power 1563, ablationtiming 1564, and optionally other ablation parameters not shown, such asphase and/or frequency. In some embodiments, contact 1565 is measuredand/or estimated based on force measurements 1566 (e.g., measurements byone or more force sensors on the ablation probe),dielectrically-measured quality of contact 1568, and/or angle 1567 ofprobe contact with tissue, which can be measured, for example, bycomparing readings from a plurality of force sensors, and/or based onindications of dielectrically measured quality of contact. Dielectricmeasurement of contact quality is described, for example inInternational Patent Application No. PCT/IB2016/052686, the contents ofwhich are included by reference herein in their entirety. In someembodiments, lesion effectiveness parameters may include the measuredposition 1537 of the probe, which can be measured, for example, based onelectromagnetic field-guided navigation 1538, dielectrically-guidednavigation 1539B, and/or imaging-based navigation 1539A.

In some embodiments, post-lesioning observations 1532 are made.Post-lesioning observations 1532 optionally include one or more ofdielectric measurements 1533 which may characterize lesion state,measurements of acute electrical isolation 1534 (that is, measurementsmade within the time of the initial ablation procedure), measurements ofpersistent electrical isolation 1536 (that is, measurements made in thedays, months, and/or years after the initial ablation procedure), and/ormeasurements made by image 1535, for example, nuclear medicine imagingof fibrotic extent. Use of dielectric measurements for characterizinglesion and/or other tissue states such as edema, is described, forexample, in International Patent Application Nos. PCT/IB2016/052690 andPCT/IB2016/052686, the contents of which are incorporated by referenceherein in their entirety. Optionally, a lesioning procedure is completedby making post lesioning observations at positions near lesions and/oralong the ablation line which the lesions define. In some embodiments,the post-lesioning observation positions are suggested automatically,for example, to check on tissue state at the locations between lesionsmost at risk for allowing electrical reconnection, and/or to check thelesions where there was is some indication of problems during theablation procedure itself.

Turning now to tissue conditions 1520, the main relevant conditions werelisted already above in relation to pre-lesioning simulations 1540, andinferred ablation results 1560. In some embodiments, at least some ofthe tissue conditions 1520 are provided as part of the lesioneffectiveness parameters 1500. For example, a lesion partiallycharacterized in the lesion effectiveness parameters by its depth may betransmural or not (and thus electrically isolating or not) depending onthe thickness 1545 of the tissue ablated. Similarly, a lesion may moreor less isolating depending on the local orientation 1525 of myocardialfibers.

In contrast, some tissue conditions shown are effectively accounted for,in some embodiments, by the parameterization of the lesion 1530. Thesecan be optionally be left out of the lesion effectiveness parametersbased on which machine learning and/or effectiveness estimation areperformed. For example, temperature measured during lesioning may affecthow a lesion is parameterized, but leave little residual effectaffecting the estimation of lesion effectiveness.

Tissue thickness 1545 is a factor governing transmurality. Thickertissue potentially requires deeper lesioning in order to get effectiveelectrical isolation. Effects of tissue thickness on lesioneffectiveness are discussed herein in relation, for example, to FIGS.2B-2C. Thickness is optionally characterized based on tissue atlasinformation 1548, and/or based on imaging measurements 1547 obtained byanalysis of anatomical images of the individual patient (obtained, e.g.,by MRI, CT, nuclear medicine, or another method). Tissue thickness 1545may be calculated and/or inferred (e.g., by machine learning methods)from dielectric measurements 1546.

Intra-lesioning temperature 1555 is optionally simulated inpre-lesioning simulations 1540, inferred from actual ablation parametersas part of inferred ablation results 1560, and/or measured 1556 duringablation. With RF ablation, for example, temperature can be anindication of ablation progress.

Similarly, edema may be simulated, inferred, or dielectrically measured1551. Edema in cardiac tissue is potentially elicited by nearbyablations, or even by rough contact with a probe. Edematous tissue ispotentially more resistant to effective ablation: for example, edematousfluid can thickens tissue so that lesion transmurality is harder toachieve.

Tissue type 1521 is optionally characterized (typically before aprocedure) by one or more imaging modalities 1522. Tissue type 1521 canalso be characterized intra-procedure by dielectric mapping 1523.Optionally, dielectric mapping may include creating a dielectric map ofa given region of the heart, for example a map of all of or a section ofa heart wall and/or heart chamber. In particular, dielectric mapping1523 (e.g., mapping based on converting impedance measurements intodielectric properties attributable to tissue positions) potentiallyallows distinguishing within a mapped region between healthy, cellulartissue, and fibrotic (possibly after ablation) tissue in which cellularstructure has been disrupted. In some embodiments, the dielectricmeasurements of tissue dielectric properties are carried out, forexample, as described in International Patent Application Nos.PCT/IB2016/052690 and/or PCT/IB2016/052686, the contents of which areincorporated by reference herein in their entirety. Electrical fields ofdifferent frequencies (and preferably also established between electrodepairs in different positions) may be generated through a body tissueregion of interest. Differences in impedance as a function of thesefield parameters may be analyzed to isolate impedance effects arisingfrom tissue regions near the electrode. The isolated impedance effectsmay be analyzed for their properties in order to identify the tissuetype that gives rise to them, for example, based on published tissueimpedance data and/or modeling.

In some embodiments, dielectric mapping is applied to create a moregeneral dielectric map, identifying tissue regions of any type havingdistinctive dielectric properties. In some embodiments, for example, themap distinguishes dielectric property differences due to tissuethickness (e.g., heart wall thickness), tissue cellular and cellularmatrix makeup (including, for example, differences due to fattydeposits, collagen, muscle fiber composition, and the like), tissuecellular integrity (intact or disrupted), fluid content (e.g., edema),other tissue layers (for example, tissues lying beyond the heart wallsuch as lung, esophagus, and/or nervous tissue), and/or surfacestructure, which can potentially affect quality of contact.

In some embodiments of the invention, the level of spatial resolution ofadjacent features provided by the dielectric mapping 1523 is betweenabout 0.1 and 1 mm. Optionally, the spatial resolution is about 0.1 mm,about 0.5 mm, 1 mm, 2 mm, 5 mm, or another larger, smaller, and/orintermediate value. In some embodiments, the map is made available fordisplay to an operator.

Finally, fiber orientation 1525 is discussed in detail herein inrelation to FIGS. 5A-5B.

Reference is now made to FIGS. 5A-5B, which schematically illustrateaspects of the planned placement of lesions 301, 302, 303, 304 relatedto myocardial fiber direction, in accordance with some exemplaryembodiments of the invention.

In some embodiments, the maximum impulse-blocking distance 305, 307between two sub-lesions 301, 302, 303, 304 centered on points 301C,302C, 303C, and 304C, respectively, is predicted in part by theorientation of myocardial fibers 66, 67 in the region of the gap. Ingeneral, fibers running parallel to the direction of impulse flow acrossthe gap can transmit impulses through a smaller gap (for example, a gapof no more than about 0.3 mm, 0.4 mm, 0.5 mm, or another distance) thanfibers running perpendicular to it (where the maximum size of aninhibiting gap may be, for example, about 1 mm, 1.5 mm, 2 mm, or anotherdistance). Discussion of the influence of fiber orientation and gap sizeon myocardial fiber impulse transmission is found, for example, inRanjan et al. (Gaps in the Ablation Line as a Potential Cause ofRecovery From Electrical Isolation and Their Visualization Using MRI.Circ Arrythm Electrophysiol 2011; 4:279-286). In the computationalmodeling of Ranjan et al., the reported maximum gap at whichconductivity failed was 1.4 mm when fiber direction was perpendicular tothe ablation line. When fiber direction was parallel to the ablationline, conductivity failure was reported only up to 0.3 mm gaps. Ranjanet al. suggest that larger gaps which appear to at least initially blockconduction in in vivo studies may include tissue with temporarilyreduced conductivity which could later recover and resume conduction.

In some embodiments of the invention, myocardial fiber orientation ismodeled from anatomical atlas data giving typical orientations, and/ormeasured for the individual patient using, for example,echocardiography-based shear wave imaging (Lee et al., Mappingmyocardial fiber orientation using echocardiography-based shear waveimaging. IEEE Trans Med Imaging 2012; 3(3):554-62), Diffusion tensormagnetic resonance imaging (Pashakhanlo et al., Myofiber Architecture ofthe Human Atria as Revealed by Submillimeter Diffusion Tensor Imaging.Circ Arrhythm Electrophysiol. 2016; 9:e004133), or another method.

Reference is now made to FIG. 3D, which is a schematic illustrationhighlighting details of a planned set of ablations creatingsub-ablations 220, and their traversal along a line of planned ablation55, in accordance with some exemplary embodiments of the invention.

Potentially, positioning for lesioning achieved by direct movementbetween adjacent lesion foci (sub-lesions, for example) is less prone toerrors and/or delays which can introduce gaps in the ablation line thatpermit impulse transmission. However, joining between the starting andstopping points of an ablation line generally requires making a joinbetween sub-lesions, where the later sub-lesion is formed up to severalminutes after the earlier one. During the interval, not only does thetissue tend to cool (which can reduce a degree of sub-lesion chaining),but there can also be an edematous response by the tissue which affectsablation effectiveness. In some embodiments, these effects are simulatedand/or estimated as part of ablation planning.

In some embodiments, an ablation plan is designed so that such joins arepositioned over regions where myocardial fiber orientation 68 (just afew patches of fiber orientation 68 are shown for illustration)generally cuts across to the direction of potential propagation acrossthe gap. For example, sub-lesion 310 is optionally a preferred candidatefor a starting lesioning position, since the local direction of fibersmeans that sub-lesion neighbor 319 can be placed with greater error,while still maintaining an effective block.

The effectiveness of an ablation lesion for creating electricalisolation is potentially affected by the direction of myocardial fibersrunning nearby: for example, fibers oriented to run through a gap (forexample, as in FIG. 5B) apparently transmit impulses more efficientlythan fibers oriented so that impulses must jump laterally from fiber tofiber to pass the gap (for example, as in FIG. 5A). Optionally, fiberorientation used in simulating or inferring ablation effects is derivedfrom atlas-based assumptions 1527 about heart wall anatomy. Additionallyor alternatively, fiber orientation is imaged 1526, for example, usingechocardiography-based shear wave imaging, and/or diffusion tensorimaging. In some embodiments, fiber orientation between two points isinferred from the rate of impulse transmission therebetween, e.g., fromimpulse timing measurements 1528.

In some embodiments of the invention, patient data parameter are alsoprovided as part of lesion effectiveness parameters 1500. Optionally,the patient data parameter include patient medical history and/orpatient vital statistics, for example, one or more of:

-   -   Patient demographics (gender, age, height, weight, body mass        index [BMI]);    -   Occupation;    -   CHAD2 Score for atrial fibrillation (AF) stroke risk (e.g.        scored as: congestive heart failure history +1, hypertension        history +1, age ≥75 years +1, diabetes mellitus history +1,        stroke or transient ischemic attack [TIA] symptoms previously        +2);    -   CHAD2VASc Score (e.g., scored as: age in years: <65 0, 65-74+1,        ≥75+2, sex: male 0, female +1, congestive heart failure history        +1, hypertension history +1, stroke/TIA/thromboembolism history        +2, vascular disease history +1, diabetes mellitus +1);    -   Family history of AF;    -   Known genetic predisposition to AF (for example mutations in the        KCNE2, KCNJ2, and/or KCNQ1 genes);    -   AF subtype characteristics (e.g., symptoms, age of first        documented episode, time since first discovered, number and        duration of episodes, need of cardioversion, hospitalization);    -   AF definition (e.g., paroxysmal, persistent, chronic persistent,        permanent);    -   Other coexisting arrhythmia (e.g., atrial flutter, accessory        pathway, atrial tachycardia);    -   Drug therapy (e.g., antiarrhythmic rate or rhythm control,        antithrombotic, anticoagulation);    -   Compliance with drug therapy;    -   Known hypercoagulability;    -   Other heart disease (e.g., structural, previous surgery,        previous cardiac catheterization, ischemic, cardiomyopathy);    -   Life style and habits;    -   Smoking;    -   Drug abuse;    -   Alcohol consumption;    -   Other comorbidities (e.g., endocrine dysfunction, malignancy);    -   Other drug therapy;    -   Autonomic nervous system state (e.g., hyperactive, normal,        hypoactive);    -   Left atrial characteristics (e.g., diameters, volume, function,        left atrial artery [LAA] shape type, presence of clot, presence        of patchy fibrosis, scars, etc.);    -   Mitral valve function (e.g., degree of regurgitation if present,        degree of floppiness if mitral valve prolapse is present);    -   Function of other cardiac valves;    -   Presence of prominent Eustachian valve;    -   presence and type of patent foramen ovale (e.g., documented        right to left or left to right inter-atrial shunt, presence of        marked inter-atrial septal aneurysmatic motion);    -   Atherosclerosis (e.g., state of carotid arteries, state of the        circle of Willis, state of coronary arteries, state of renal        arteries);    -   Peripheral artery disease;    -   History of deep vein thrombosis or pulmonary emboli (long seated        position syndrome);    -   Characteristics of self-healing process (e.g., levels of        angiogenesis growth factors, scar formation)    -   Level of myocardial injury biomarkers following the cardiac        ablation procedure (e.g., creatinine kinase [CK], myocardial        bound for CK [CK-MB], Troponin-I [TnI]);    -   Heart rhythm during the ablation procedure (normal sinus rhythm        [NSR], atrial fibrillation, or atrial flutter) and/or need for        cardioversion during the procedure;    -   Recurrence of AF post-ablation (e.g., timing, symptoms, duration        of episodes, need for cardioversion, drug therapy);    -   Remodeling of left atrium post-cardiac ablation (documented on        CT/MR imaging);    -   Left atrium stunning post ablation (documented on        electrocardiography, e.g. TTE or TEE);    -   Number and duration of hospitalizations post ablation    -   Stroke or documented brain MR lesions post ablation; and/or    -   Quality of life post-ablation (subjective questionnaire).

Optionally, data indicating therapeutic strategy and/or events areincluded in the lesion effectiveness parameters 1500, for example:

-   -   Pulmonary vein isolation (PVI) only    -   PVI together with an additional roof or mitral ablation line;    -   PVI+focal ablations, e.g. due to complex fractionated atrial        electrograms, dominant frequency and/or rotors;    -   Neuromodulation (ganglionated plexi ablation);    -   Number of ablation points;    -   Procedure time; and/or    -   Immediate complication (e.g. cardiac perforation, tamponade,        esophageal lesions, phrenic nerve palsy).

Context-Adjusted Lesion Effectiveness Estimator

Reference is now made to FIG. 4B, which schematically represents a rangeof options for prior inputs to a lesion effectiveness estimator and formachine learning of the estimator, comprising parameters potentiallyrelating to lesion effectiveness, including prior inputs definingconditions for lesion effectiveness, according to some embodiments ofthe present disclosure.

For simplicity, FIG. 4B is abbreviated from FIG. 4A to show just the twomain divisions of tissue condition 1520 and parameterized lesion 1530;however, any of the parameters descriptions mentioned in relation toFIG. 4A should also be considered as included within this figure. Thesubstantial difference from FIG. 4A is that the lesion effectivenessparameters 1500A may be supplemented by an optional requirements node:effectiveness constraints 1510. Optionally, lesion effectivenessparameters 1500A also includes any parameters described with respect topatient data parameter and/or ablation strategy in regards to lesioneffectiveness parameters 1500.

Effectiveness constraints 1510 are optionally derived, for example, fromplanned lesion spacing 1511 and/or observed (for example, dielectricallymeasured) lesion spacing 1512. Planned lesion spacing 1511 and/orobserved lesion spacing 1512 may be expressed in terms of distances (forexample, when lesion shape is geometrically defined as a basis formachine learning), but can also be otherwise defined, e.g., as aconstraint on the minimum strength of a dielectric signal indicatingfibrotic tissue under the electrode.

Optionally, effectiveness constraints include regions where lesioningshould be avoided (avoided regions 1513). These places optionallyinclude, for example, lesions located too near to the esophagus, toonear the phrenic nerve, and/or too near the venous roots. Effectivenessconstraints such as avoided region 1513 optionally provide a potentialadvantage, e.g. for indicating whether or not an ablation plan is safe,and/or for indicating a potential for complications, in case theablation procedure has already been performed. Additionally oralternatively, other safety constraints are used; for example,constraints on maximum power settings, maximum duration of ablation,etc., which, if exceeded, could indicate an increased potential forcomplications during and/or after a procedure.

A potential effect of adding effectiveness constraints to the lesioneffectiveness parameters 1500A is to allow the machine learning processto incorporate some information about local tissue context into theeffectiveness estimator. A lesion's effect on electrical isolationpotentially depends not only on being transmural, but also on beingjoined up to adjacent lesion positions. Effectively, this requirementmay allow the machine learning process to distinguish between lesions ofthe same diameter, where the effective lesion meets the required sizefor its position (that is, contacts its neighbors), but the ineffectivelesion does not contact its neighbors, and so fails to provide effectiveelectrical isolation.

This local-context approach, which may be applied to every sub-lesion inan ablation line potentially provides a way to judge the overalleffectiveness of electrical isolation of the ablation line, emergentfrom estimation of the effectiveness of each sub-lesion. Alternatively,estimation overall ablation line effectiveness may be treated as aseparate problem, either independently calculated, or calculated incascade with estimations of individual sub-lesion effectiveness.

Estimators for Ablation Line Effectiveness

Reference is now made to FIG. 6 , which is a schematic flowchart of amethod of deriving and applying an estimator for predicting ablationline effectiveness, according to some embodiments of the presentdisclosure.

Conceptually, the blocks of FIG. 6 correspond to the blocks of FIG. 1Bwith the substitution of ablation line effectiveness (that is, theeffectiveness of a linked group of lesions, e.g., at creating electricalisolation) for individual lesion effectiveness. Ablation lineeffectiveness parameters 1700 are provided, e.g., as described inrelation to FIGS. 10A-10B. Machine learning at block 1803 may be appliedto collections of ablation line effectiveness parameters 1700 assignedto a training set, each with a corresponding observed ablation lineeffectiveness 1802, and optionally based on an ablation line model 1801.Machine learning at block 1803 may be in accordance with techniquesdescribed in relation to machine learning 1603. Ablation lineeffectiveness parameters 1700 may include one or more parametersdescribed in relation to lesion effectiveness parameters 1500 or 1500A,for example as described in relation to FIGS. 4A and 4B. Ablation lineeffectiveness parameters 1700 may include dielectric measurements.Ablation line effectiveness parameters 1700 may include indications ofconditions of formation of an ablation line and/or sub-lesions of theablation line; for example, lesion placement, ablation tool settingsgoverning sub-lesion formation (such as ablation power, dielectricquality of contact, angle of contact, force of contact, and timing ofablation), and/or tissue conditions within which the sub-lesion issituated. Ablation line effectiveness parameters 1700 may includeindications of structure of an ablation line (e.g., depth, size, volumeof tissue etc).

The resulting learned ablation line estimator 1804 is the applied atblock 1805 to other collections of ablation line effectivenessparameters 1700, producing estimates of ablation line effectiveness1806. Other collections of ablation line effectiveness parameters 1700may refer to ablation line effectiveness parameters 1700 collected afterlearned ablation line estimator 1804 was created, e.g., ablation lineeffectiveness parameters 1700 collected in run-time during a medicalprocedure. Other collections of ablation line effectiveness parameters1700 may include dielectric measurements measured run-time during amedical procedure. Such dielectric measurements may be used as inputs tolearned ablation line estimator 1804 to obtain estimated ablation lineeffectiveness 1806.

It should be noted that learned ablation line estimator 1804 may beupdated and/or adjusted based on such other collections of ablation lineeffectiveness parameters 1700.

In some embodiments, the estimator output (e.g., estimated ablation lineeffectiveness) may include a binary prediction (e.g., +1 or −1). In someembodiments, the estimator output (e.g., estimated ablation lineeffectiveness) may include a probabilistic output, e.g., between zeroand one (e.g., a dynamic score). Such embodiments may be advantageouswhen using estimator during an ablation procedure. For example, they mayfacilitate a user decision whether to proceed or complete the procedureand/or to adjust the ablation plan.

Estimated ablation line effectiveness 1806 may be presented to a user(e.g., physician) during a medical procedure. Such estimated lesioneffectiveness 1806 may be used to adjust an ablation plan.

In the context of ablation lines, effectiveness also applies relative tocriteria of electrical isolation, lesion persistence, and/or freedomfrom disease at various time points, as also described in relation toFIG. 1B. Optionally, estimates of ablation line effectiveness 1806 areaccompanied by a metric of certainty, for example, sensitivity and/orselectivity. Estimated ablation line effectiveness 1806 optionally isconfigured to identify the most likely location or locations along theablation line where incomplete electrical isolation occurs or willoccur. In some embodiments, estimated ablation line effectiveness 1806is displayed within an interactive user interface allowing an operatorto examine the estimate of ablation line effectiveness from differentperspectives: for example, to check certainty of results, to checkestimated lesion effectiveness 1606 which may correspond to estimatedposition of failed electrical isolation along the ablation line, tocompare the current ablation line with other ablation lines in thedatabase which historically have produced similar estimator results,etc.

The primary difference from FIG. 1B is that the estimator now is able totake into account the whole context of a line of lesions, rather thaneach lesion individually, or lesions individually within a localcontext. For example, there may be a lesion which lesion-only estimationscores as ineffective (even taking into account local context), perhapsbecause one lesion is not transmural, leaving a possible transmissiongap. However, if the placement of other lesions (optionally includingconsideration of pre-existing scar tissue) is such that no effectiveelectrical impulse can reach the allowed gap, then an ablation lineestimator is potentially be able to detect that the overall ablationline remains effective.

Also, in some embodiments, an estimator built around features of theablation line as a whole is potentially more convenient for the analysisof gaps between lesions. For example, since gaps can optionally bedescribed directly in the ablation line effectiveness parameters, andtheir behavior even modeled, rather than be relegated to being theimplicit consequence of inadequate ablation size or placement.

Reference is now made to FIGS. 7A-7B, which schematically illustrateadjacency effects of tissue lesions 501, 502, 503 made in two differentsequences, in accordance with some exemplary embodiments of theinvention.

A superficial extent of sub-lesion 501 is represented by the outercircle, while progressively smaller interior circles 501A, 501B, 501Crepresent lesion extent at gradually increasing depths (depth issimilarly represented for sub-lesions 502 and 503). In some embodiments,after a first lesion 501 is made, a second lesion 502 is made only afterthe elapse of a cool-down period. Then the two lesions potentially aremade as if independent from one another. Unless care is taken to ensuresufficient overlap, this can increase the potential for animpulse-permissive gap, particularly in the deeper layers.

In FIG. 7B, however, sub-lesion 503 is placed almost immediately aftercreation of lesion 501, while there remains some residual heating fromthe previous ablation. In this case, thermal simulation may show thatthe two sub-lesions will tend to merge, for example as shown at region505.

The ablation effect (e.g., the lesion effectiveness) of a firstsub-lesion may be estimated, for example as described in reference toFIG. 1A, e.g., using estimator 1604. For example, lesion effectivenessparameters and/or lesioning effects on tissue parameters may be inputsto an effectiveness estimator (e.g., estimator 1604) before secondsub-lesion ablation occurs. Optionally, such estimating facilitatesdefining the sub-lesioning sequence; e.g., the order and/or timing bywhich sub-lesions are ablated.

Chained Lesion and Ablation Line Effectiveness Estimators

Reference is now made to FIG. 9 , which is a schematic flowchart of amethod of chaining estimators predicting lesion effectiveness andablation line effectiveness into a sequence, according to someembodiments of the present disclosure.

FIG. 9 comprises a chaining together of the estimation pathway portionsof FIGS. 1B and 6 , and the meaning of the blocks shared with them isthe same. The difference is in the chaining. An estimated lesioneffectiveness 1606 for each lesion in an ablation line may be collectedin an estimation array 1706 of lesion effectiveness estimates. Thenarray 1706 may be provided in turn as part of the ablation lineeffectiveness parameters 1700, so that the estimation array 1706 becomespart of the input data, based on which the estimator function operates.It should be understood that in such an example, learned ablation lineestimator 1804 was previously learned using training set data includingexamples of estimation array 1706.

In some embodiments, an effect of adding a second stage of estimatorprocessing (ablation line effectiveness estimator chained onto resultsof a lesion effectiveness estimator) is to correct potential limitationsof lesion-based estimations of effectiveness. For example, twoapparently effective lesions (according to a lesion effectivenessestimator) may actually be too separated from one another to createelectrical isolation. Conversely, as mentioned in relation to FIG. 6 ,an apparently ineffective lesion is potentially located where the gapthat it allows is inaccessible to electrical impulses of the heart—andso the gap is functionally harmless, and the ablation line ispotentially effective after all.

Reference is now made to FIG. 10A, which schematically representsoptions for prior inputs to an ablation line effectiveness estimator1804 and for machine learning of estimator 1804, comprising parameterspotentially relating to ablation line effectiveness, including inputscomprising output of lesion effectiveness estimator 1604, according tosome embodiments of the present disclosure. Output of lesioneffectiveness estimator 1604 may include estimated lesion effectiveness1606 and/or estimation array 1706.

Ablation line effectiveness parameters 1700 of FIG. 10A compriseparameters which are optionally supplied to the methods of FIGS. 6 and 9as a basis for machine learning of an ablation line effectivenessestimator 1804, and/or for use of estimator 1804. Optionally, ablationline effectiveness parameters 1700 also includes any parametersdescribed with respect to patient data parameters and/or ablationstrategy in regards to lesion effectiveness parameters 1500. In someembodiments, ablation line effectiveness parameters 1700 may includedielectric measurements.

Ablation line effectiveness parameters 1700 of FIG. 10A may compriseTissue conditions 1720 and/or parameterized gaps 1730.

Tissue conditions 1720 optionally comprise any suitable combination ofthe tissue condition parameters discussed in relation to FIG. 4A, oranother parameter indicating tissue condition. Similarly to as in FIG.4A, the parameters indicating tissue conditions 1720 are optionallyconsidered as supplying information which can be weighted in the learnedestimator to modulate the estimated effectiveness of the ablation lineitself.

Compared to parameters used as lesion effectiveness parameters 1500,ablation line effectiveness parameters 1700 optionally include moreinformation relating to global tissue structure. For example, data aboutfiber orientation 1525 is optionally incorporated not just for tissueimmediately surrounding lesions, but also for regions that electricalimpulses cross to reach the lesions. Similarly, measurements of impulsepropagation over these regions are optionally incorporated into theablation line effectiveness parameters. This potentially allows morerefined prediction of the functional implication of gaps, derived, forexample, from information about the strength of impulses that reachthose gaps.

In some embodiments, parameterized gaps 1730 optionally includes anysuitable combination of the parameters already discussed in relation tothe parameterized lesions 1530 of FIG. 4A. However, the measurements areoptionally parameterized in terms of characterizing gaps (or potentialgaps) between lesions, rather than the lesions themselves (for whichestimator results are already available).

Additionally or alternatively, data used to form parameterized gaps 1730comprises dielectric characterization of regions into which lesions donot extend, and/or extend with incomplete electrical isolation.Optionally, gap parameter information comprises observations (e.g.,measured from catheter electrodes) of places where electrical isolationis not achieved. Optionally, measurements delineate the extent ofelectrical isolation between lesions. Potentially, an early extent ofimpulse activity, even if it is presently electrically isolated, isindicative (e.g., because of how far it intrudes into a region of theablation line) of possible later loss of electrical isolation as tissuerecovers from a procedure. It should be understood that a parameterrelating to a gap is optionally reconfigured as a parameter relating toa lesion (for example, by adding constraint parameters on lesion size,as described in relation to FIG. 4B, herein). In either case, theavailability of input data giving at least relative lesion placements isimportant for the estimation of overall integrity of the electricalisolation created by the lesion line. This may be derived, for example,from the absolute shape of the ablation line, from the size of regionsof mutual overlap for each lesion, from relative distances for eachlesion from its neighbors combined with measured and/or inferreddiameter, etc.

Finally, estimation array 1706 may be used, in some embodiments, toprovide information relating to the effectiveness of electricalisolation, and/or the predicted persistence, of the lesions themselves.

Stand-Alone Ablation Line Effectiveness Estimator

Reference is now made to FIG. 10B, which schematically representsoptions for prior inputs to an ablation line effectiveness estimator andfor machine learning of the estimator, comprising parameters potentiallyrelating to ablation line effectiveness for use with a stand-aloneablation line effectiveness estimator according to some embodiments ofthe present disclosure.

FIG. 10B indicates an alternative implementation of ablation lineeffectiveness parameters 1700A, used, for example, in some embodimentsof the present invention wherein the method of FIG. 6 for generating andusing learned ablation line estimator 1804 is used standalone, withoutchaining to the results providing an estimated lesion effectiveness1606. Optionally, ablation line effectiveness parameters 1700A alsoinclude any parameters described with respect to patient data parametersand/or ablation strategy in regards to lesion effectiveness parameters1500.

Rather than including estimation array 1706, ablation line effectivenessparameters 1700A optionally directly include lesion parameterizationarray 1740 parameterising a plurality of lesions making up the ablationline. Lesion parameterization array 1740 may include an array ofparameterized lesions 1530.

It should be understood that the parameter configurations of FIGS. 10Aand 10B represent two ends of a spectrum. In some embodiments, bothlesion parameterization array 1740 and array 1706 of estimated lesioneffectiveness are used in combination. Moreover, the role ofparameterized gaps 1730 in FIGS. 10A-10B is optionally replaced byextended parameterizations of the lesions, e.g., as allowing or notallowing space between each other.

Estimator Uses

Reference is now made to FIG. 11 , which schematically representsdifferent periods for acquiring prior inputs to estimators, and theirrelationship to different periods for acquiring feedback inputs toestimators, according to some embodiments of the present disclosure.

FIG. 11 illustrates an organizing principle mentioned in the discussionof FIG. 4A: that data sources for parameters may be assigned to one ormore of the three time stages. The three stages are shown here aspre-procedure planning 2001, intra-procedure data and/or measurement2003, and post-procedure measurement 2005.

Shown in the bottom row of FIG. 11 are three exemplary effectivenessestimations, each for a different point in time: at the time of theablation procedure (t=0, block 2010), and for a plurality of times afterthe ablation procedure (t=1 . . . t=n; blocks 2012 and 2014). Estimators2010, 2012 and 2014 may include any of the estimators described above;for example: lesion estimator 1603 and/or ablation line estimator 1804and/or ablation segment estimator 2104 and/or edema estimator 2604.

Ideally, the only estimator needed would be the final one for t=n (forexample, estimation of effectiveness at five years post-procedure),since permanent treatment of disease is the end goal. Practically,however, each estimator may find a use in different stages ofpre-procedure planning, adjustment of ablation strategy during theprocedure, and/or post-procedure follow-up. A plurality of estimatorsmay be created to target different information—for example, to target asingle outcome aspect (e.g., transmission blockage as such), or anysuitable combination of outcome aspects (e.g., transmurality, intersub-lesion connection, success at ablating before edema sets in, etc.).There is a potential advantage of single-aspect estimators in simplicityof implementation. There is also a potential advantage forinterpretation, since knowing what specific portion of a procedure isproblematic helps to target planning of mitigation and/or remediation.The three general stages are now discussed in turn.

Estimator use in pre-procedure planning: At the pre-procedure planningstage 2001, short-term estimations of the effectiveness of an ablationplan in achieving electrical isolation are potentially the most useful.In particular, there may be more detailed feedback information availablefor short-term estimator; particularly for estimators based on outcomesmeasured during the ablation procedure, and/or outcomes measured duringa period of more active treatment and follow-up soon after a procedure.

However, it is potentially helpful to be informed that there is at leasta possibility of long-term effectiveness for a particular plan, eventhough there remain a large number of unresolved variables (e.g., incarrying out the procedure itself) that could make estimation of likelyfuture effectiveness difficult. Potentially, moreover, there are hiddenlong-term consequences of a plan—for example, lesions placed in a heartregion which is, perhaps unknowingly, difficult to characterize.Long-term feedback data can potentially indicate that there is someproblem related to such lesions. Such hidden problems may potentiallyresult a lowered estimate of effectiveness, even if the physiologicalreason for the lowered estimate remains unknown.

Estimator use during a procedure: During the procedure itself (block2003), estimator results are optionally updated regularly as newablations are performed. Re-estimation is performed, for example, afteror before every ablation of a sub-lesion and/or completion of anablation segment (e.g., completion of the second of two sub-lesionsdefining an ablation segment), or after any suitable number of ablationssufficient to get useful data, for example, after at least 10-30ablations. Estimations are optionally refreshed every time new databecomes available, or optionally every few seconds or minutes (e.g. 5seconds, 10 seconds, 30 seconds, 1 minute, 2 minutes, 5 minutes oranother longer, shorter or intermediate period). Since the estimation isby then largely based on effectiveness estimated from actualmeasurements and events, long-term estimates may begin to be moreaccurate. However, short-term estimators developed from detailedfeedback data may again provide more useful feedback for evaluatingpotential adjustments to a procedur—e.g., which particular lesions areinsufficient and should be revisited, or which parts of the ablationline are vulnerable to developing gaps.

Estimator-driven adjustments (e.g., adjustment of an ablation plan) areoptionally manually guided (e.g., a computer shows an estimator resultto an operator, who decides how to proceed), or automatic. The followingis an example of an automatic adjustment:

Upon an operator selecting the position of the next ablation operation(such as: next lesion position), the ablation monitoring and/ortreatment system optionally applies an estimator to predicteffectiveness of the result using not only the current settings, butalso other settings available from within current conditions—forexample, different power levels, times of ablation, frequencies, phases,angles of approach and the like. This can be considered as a way ofsearching an envelope of possible ablation parameter settings in orderto select the parameter settings which seem most likely to lead to atargeted effectiveness outcome. Optionally, the estimation is providedfor effectiveness in a single respect (e.g., transmurality).Alternatively, estimation considers effectiveness in a plurality ofrespects such as safety, transmurality, gap-free connection withadjacent lesions, permanence of the lesion, gap-free connection betweenadjacent sub-lesions of an ablation segment, permanence of the ablationsegment, etc.). Optionally, the different respects are at leastpartially in competition to each other (a large lesion may be less safe,for example).

An estimator feature potentially of particular use during ablation isestimation of the effects of edema on ablation. An initial ablation planmay attempt to account for edema by simply lesioning in a line, so thatmost ablations happen too quickly for edema from neighboring ablationsto be a factor. However, adjusting an ablation plan to mitigate apossible gap in isolation may involve returning to a point whereablation has had time to become well-developed. In some embodiments,estimation of edema state can be used to find an optimal location for arepair ablation, and/or to reconfigure ablation parameters so thateffective ablation is more likely.

Estimator use after a procedure: Once the post-procedure clinicalsituation has stabilized, the long-term estimators potentially become ofmore practical use, as key medical decisions also become more strategicin nature. An estimator looking months or years in advance at thelikelihood of disease recurrence can help a clinician advise a patientwhen the next follow-up session should occur, and/or help to plan for anapparently likely eventuality of the return of disease. In someembodiments, an ablation segment effectiveness estimator estimates alikelihood (for example, a percentage likelihood, or another prediction,optionally including an estimate of specificity and/or sensitivity) ofeffective block for one or more ablation segments within a week, amonth, three months, six months, a year, two years, five years, or anyother suitable period after the procedure that created the ablationsegment.

Estimators for Ablation Segment Effectiveness

For purposes of describing ablation segment effectiveness estimatorsherein, an ablation segment comprises any planned or actual segment of alonger ablation line selected for evaluation by an estimator. Anablation line, in some embodiments, comprises a group of one or moresub-lesions introduced together by ablation to achieve a clinicalresult. Typically a plurality of sub-lesions are selected. Two or moreadjacent and/or overlapping sub-lesions along an ablation line may bereferred as ablation segment.

Typically, the sub-lesions are introduced as a chain of ablationsextending along a pathway, with the target of creating an uninterruptedbarrier to electrical transmission from one side of the line to theother. In some embodiments, an ablation segment comprises at least twosub-lesions in proximity. “Effectiveness” of such a segment, in someembodiments, is defined as next described.

In some embodiments of the invention, an ablation segment effectivenessestimator is used to predict acute (e.g., immediate) and/or persistent(e.g., over a period of at least 1 month, 3 months, 6 months, 1 year, 2years, 5 years, or another longer, shorter, or intermediate period)effectiveness of an ablation segment; optionally either a planned oractual ablation segment. Effectiveness of a segment, in someembodiments, relates to properties of the segment's sub-lesions, regionswhich may exist between those sub-lesions, and/or the extent ofsub-lesions relative to the overall thickness of the ablation tissue(effective transmurality). Effectiveness of an ablation segment, in someembodiments, comprises completeness of electrical isolation across,under, and/or over the segment. It should be noted that it is difficultto directly validate ablation success in humans, since direct visualinspection of sub-lesions in a living patient (e.g., for transmurality,interconnectedness, etc.) is unavailable. Accordingly, it is a potentialadvantage to make use of a wide variety of indirect and/or incompletedata sources which at least partially indicate (e.g. correlate with)procedure success, in order to supply a greater level of certainty forplanning of procedures, correction of ongoing procedures, and/orprediction of procedure outcomes.

Reference is now made to FIG. 14A, which is a schematic flowchart of amethod of deriving and applying an ablation segment effectivenessestimator for predicting ablation segment effectiveness, according tosome embodiments of the present disclosure. First, inputs to andoperation of machine learning at block 2103 are described. Thenapplication of a learned ablation segment effectiveness estimator 2104at block 2105 is described (in FIG. 14A and other figures herein, both“segment estimator” and “ablation segment effectiveness estimator” arealso used to refer to an ablation segment effectiveness estimator).

In some embodiments of the invention, segment estimator application atblock 2105 may use a segment estimator in which segment lesioneffectiveness may be estimated based on considered formation and/orcharacteristics and/or effectiveness of each sub-lesion forming thesegment. In some embodiments, the interaction between the sub-lesionsforming the segment is also taken into account for estimating thesegment lesion effectiveness.

For example, a two sub-lesion ablation segment, in some embodiments, isdefined as comprising sub-lesion 1 and sub-lesion 2. Then the segmentlesion effectiveness is optionally determined from a parameter-combiningestimator function which may be expressed as E=f(SA₁,SA₂,SI_(1,2)),wherein SA₁ and SA₂ represent scores of ablation which indicate theindividually considered formation and/or characteristics of eachsub-lesion (individual lesion parameters), and SI_(1,2) represents ascore of interaction indicative of interactions between the twoindividual sub-lesions (lesion interaction parameters) that affect theireffectiveness at creating a blocking ablation segment. In someembodiments, segment lesion effectiveness may be estimated based onindividual lesion parameters of two or more sub-lesions forming thesegment (e.g., each sub-lesion forming the segment) and lesioninteraction parameters of the two or more sub-lesions.

In some embodiments, SI_(1,2) may be a function of distance between thesub-lesions (optionally simply the distance itself). In someembodiments, SI_(1,2) may be a function of an interval of time betweenablation of the two sub-lesions (optionally the time itself). In someembodiments, SI_(1,2) is a function of both. In some embodiments,additional factors may be used such as local thermal properties,myocardial fiber orientation, and/or tissue wall thickness.

Thus, for example, score of interaction SI_(1,2) is optionallyimplemented as SI_(1,2)=d_(1,2) (when distance is the lesion interactionparameter); or SI_(1,2)=t_(m)k/(t_(m)k−min(t_(1,2),t_(m))) (where timeis the lesion interaction parameter); or (in an example of combiningboth time and distance as lesion interaction parameters)SI_(1,2)=d_(1,2)t_(m)k/(t_(m)k−min(t_(1,2),t_(m))); where d_(1,2) is thedistance (e.g., Euclidean distance) between the two sub-lesions (e.g.,in mm), t_(1,2) is the time interval between the two sub-lesions (e.g.,in minutes), and t_(m), is a constant time in minutes (for example, 15minutes), which may be understood in that case as modeling thedevelopment of edema as an increasing effect over about 15 minutes. Theterm involving t_(m), is optionally constructed in another form giving adifferent (e.g., exponential, logarithmic, linear, or other) time-courseto the development of edema; the form given is an example. In thecombined example, the factor k may optionally be interpreted asrepresenting the degree to which maximally developed edema acts toincrease the effective distance d_(1,2) (under this interpretation, thedistance d_(1,2) “looks larger” when edema is more developed, because ofthe difficulty of ablation in edematous conditions, for example). Forexample, k=2. There is no particular requirement for any of thesespecific functional forms, selection of constant values, orinterpretation of terms; they are given for the sake of example.Optionally, users are provided (by a system implementing this type ofestimator) with the means to define a function suited to theirparticular experiences.

The score of ablation can likewise be selected—optionally by theuser—based on any suitable criteria related to the ablations themselves.For example, the Carto 3 VISITA™ Module (K133916) provides access todata collected during the application of RF energy; the method gives adifferent weight to certain recorded parameters and presents theirvalues as well as a composite value to the operator. For the sake of thepresent explanation, the composite value may be understood as beingscaled to 0 (non-transmural) or 1 (transmural). Optionally, this scalingis modified by further data, for example, the estimated state of localedema formation and/or estimated local wall thickness.

Then combining estimator function E=f(SA₁,SA₂,SI_(1,2)) is optionallydefined as 0 (estimate of failed effectiveness) when either ablation 1or ablation 2 is 0, and otherwise as 1 (estimate of successfuleffectiveness); but only if the “effective distance” SI_(1,2) (or otherscore of interaction; the interpretation as “effective distance” is forpurposes of explanation but not essential) is below some threshold,e.g., SI_(1,2)<d_(max), where d_(max) is, for example, suitably setbetween about 1-10 mm, or between 1-5 mm or 1-3 mm. In some embodiments,a threshold for a score of interaction (for example: threshold foreffective distance) may be adjusted (e.g., dynamically) during theprocedure. Optionally, the adjustment may be a function of or mayotherwise depend on the score of ablation of the sub-lesions. Forexample, the threshold for effective distance may be smaller forsub-lesions having higher score of ablation (e.g., sub-lesionsdetermined to be effective).

In some embodiments, the portion of the estimator SI_(1,2) (whether ornot it is interpretable in terms of “effective distance”) is defined asa non-linear function of a plurality of parameters. For example, theeffects of edema on segment effectiveness are optionally themselvesmodeled as a function of sub-lesion distance. Optionally, SI_(1,2) isdefined as a function yielding graded output (that is, output which canassume more values than a bivalued effective/ineffective result;optionally values in a continuous range).

Optionally, any aspect of the score parameters or of theparameter-combining estimator function itself is defined differently fora plurality of different areas in a heart chamber, for example, for theleft or right pulmonary veins, and/or for the front or back walls of theheart chamber.

A potential advantage of the machine learning method of generating anestimator is that to allow the inherent statistical properties of theeffectiveness of previous procedures to control how the segmentestimator is constructed, rather than a priori selection of theoreticalmodel form and weighting criteria. Of course, a machine-learnedestimator may optionally be approximated by a function-type algorithmonce already established.

According to some embodiments, estimating outcome of ablation treatmenteffectiveness (e.g., segment lesion effectiveness) comprises: receivinginput data indicating conditions of formation of a plurality ofsub-lesions making together an ablation segment in a tissue (e.g., heartwall) and estimating an effectiveness of the ablation segment inblocking electrical signals from crossing across the ablation segmentbased on the conditions of formation of the plurality of sub-lesions. Insome embodiments, the estimated effectiveness of the ablation segment isindicated to a user (e.g., a physician), for example: it may displayed,and/or used to trigger an automatic recommendation to place anothersub-lesion and/or adjust an existing sub-lesion. Input data indicatingconditions of formation of a plurality of sub-lesions may include one ormore sub-lesion characterizing parameters.

Optionally, the input data includes dielectric measurements. Accordingto some embodiments, estimating outcome of ablation treatmenteffectiveness (e.g., segment lesion effectiveness) comprises: receivingdielectric measurements indicating conditions of formation of aplurality of sub-lesions making together an ablation segment in a tissue(e.g., heart wall) and estimating an effectiveness of the ablationsegment. In some embodiments, the estimated effectiveness of theablation segment is indicated to a user (e.g., a physician), forexample: it may displayed, and/or used to trigger an automaticrecommendation to place another sub-lesion and/or adjust an existingsub-lesion. In some embodiments, effectiveness of the ablation segmentcomprises blocking electrical signals from crossing across the ablationsegment.

Prior Inputs to Machine Learning

In some embodiments, the block marked 2100 in FIG. 14A comprises aplurality of “collections” of ablation segment effectiveness parameters2100. Each such collection in turn comprises a heterogeneous set of datainputs relating to one or more particular ablation segments. Theparticular ablation segment is formed (and/or is planned to be formed)by the operation of an ablation modality (e.g., radio frequencyablation, cryoablation, microwave ablation, laser ablation, irreversibleelectroporation, substance injection ablation, and high-intensityfocused ultrasound ablation) acting on tissue which is targeted forablation. The data inputs of a collection of ablation segmenteffectiveness parameters 2100 may include one or more of: sub-lesionmeasurements along the ablation segment (e.g., sub-lesion characterizingparameter having different values among a plurality of sub-lesions),placement of sub-lesions comprising the ablation segment, ablation tool(e.g., ablation catheter) settings governing sub-lesion and/or ablationsegment formation, calculated results of ablation (e.g., effectivenessof two or more sub-lesions forming the ablation segment), distance oranother spatial relationship of the sub-lesions (optionally simply thedistance itself), interval of time between ablation of the sub-lesions(optionally the time itself) or another temporal relation between thesub-lesions and tissue conditions within which the ablation segment issituated. Optionally, ablation segment effectiveness parameters 2100include other data; for example: patient data parameters, and/orpreviously acquired ablation data for the same patient. Further detailsof ablation segment effectiveness parameters 2100 are provided indescriptions with reference to FIG. 14B herein. Ablation segmenteffectiveness parameters 2100 may include dielectric measurements.Ablation segment effectiveness parameters 2100 may include indicationsof conditions of formation of the ablation segment; for example, lesionplacement, ablation tool settings governing lesion formation (such asablation power, dielectric quality of contact, angle of contact, forceof contact, and timing of ablation), and/or tissue conditions withinwhich the lesion is situated. Ablation segment effectiveness parameters2100 may include sub-lesion measurements along the ablation segment(e.g., effectiveness of two or more sub-lesions forming the ablationsegment), distance or another spatial relation between the sub-lesions(optionally simply the distance itself), interval of time betweenablation of the sub-lesions (optionally the time itself) or anothertemporal relation between the sub-lesions. Ablation segmenteffectiveness parameters 2100 may include indications of structure of anablation segment (e.g., depth, size, volume of tissue etc.), indicationsof structure of one or more sub-lesions forming the segment and/orinteraction between sub-lesions.

The inputs of each collection of ablation segment effectivenessparameters 2100 may at least potentially indicate (individually and/orin aggregate) information about the “effectiveness” of the particularablation segment to which they relate. In some embodiments of thepresent invention, “effectiveness” is defined in relation to targetedoutcomes of cardiac ablation treatments for atrial fibrillation (AF). InAF ablation treatments, an effective ablation segment comprises at leastone, and preferably a plurality of sub-lesions in a cardiac wall (i.e.,an atrial wall) which together substantially block electrophysiologicalimpulse transmission from passing through, over, and/or under theablation segment.

In some embodiments, persistence (or permanence) of the ablationsegment, its sub-lesions, and/or its impulse blocking characteristics isa criterion of effectiveness. Other definitions of “effectiveness”applicable in some embodiments of the present invention are describedhereinbelow. In some embodiments, effectiveness relates to conditions ofsafety. Optionally, for example, “effectiveness” is at least partiallydefined as comprising avoidance of some particular outcome, for example,avoiding lesioning of the esophagus, the phrenic nerve, and/or thevenous roots, which could lead to serious complications. Optionally, anablation segment effectiveness estimator is used for any combination of“effectiveness” conditions; additionally or alternatively, a pluralityof ablation segment effectiveness estimators is used, which optionallyeach separately cover a one or more criteria of ablation segmenteffectiveness.

At block 2103, in some embodiments, a plurality of collections ofablation segment effectiveness parameters 2100 is provided as trainingset data for use in one or more machine learning methods, in order togenerate a learned ablation segment effectiveness estimator 2104(herein, black dots mark the usual receiving side of a connecting line,e.g., direction of data communication; however, use of this conventiondoes not exclude two-way communication). The example of “machinelearning” is used herein as an example of a method of creating anestimator, and should not be considered limiting. Alternatives include,for example general purpose statistical methods and/or estimatordefinition based on theoretical equations accounting for observedcorrelations. In some embodiments, training set data used in generatingthe learned ablation segment effectiveness estimator 2104 are obtainedin vivo. In some embodiments, at least part of the training set dataused to generate the learned ablation segment effectiveness estimatorare obtained in vitro, for example based on ablation of porcine heartwall. Machine learning at block 2103 may be in accordance withtechniques described in relation to machine learning 1603. In someembodiments, an ablation segment effectiveness estimator may be trained(for example as explained in block 2103) from training set dataincluding collections of ablation segment effectiveness parameters 2100with observed lesion effectiveness 1602; to provide estimated lesioneffectiveness 1606; for example: output of ablation segmenteffectiveness estimator 2104 may be an indication of the transmuralityof a single lesion.

In some embodiments, training set data including collections of ablationsegment effectiveness parameters 2100 may include examples of positiveand negative outcomes. Positive outcome (in other words: effectiveablation) may indicate that the ablation segment (e.g., two ablationpoints) is transmural. Negative outcome can happen for example: as aresult of one non-transmural lesion and/or a non-contiguous ablationsegment.

Feedback Inputs to Machine Learning

In some embodiments of the invention, for each collection of ablationsegment effectiveness parameters 2100 in the training set data, there isalso provided as input to block 2103 a corresponding data collectionindicating observed ablation segment effectiveness 2102. For example,the observed ablation segment effectiveness optionally includes ameasurement showing one or more of the following:

-   -   acute electrical isolation is established by the ablation        segment at the end of the ablation procedure;    -   persistent electrical isolation remains for at least 5 days, 30        days, 60 days, 90 days, or another longer, shorter, or        intermediate duration after the end of the ablation procedure;    -   asymptomatic;    -   hospitalization;    -   survival;    -   AF burden <5%;    -   a fibrotic area corresponding to the ablation segment persists        for at least 5, 30, 60, 90, or another longer, shorter, or        intermediate period after the end of the ablation procedure;        and/or    -   the patient is disease free (at least with respect to conduction        across a particular ablation segment) for at least 1 month, 3        months, 6 months, 1 year, 2 years, 5 years, or another longer,        shorter or intermediate period after the end of the ablation        procedure.

In some embodiments, observed ablation segment effectiveness 2102relates to safety—for example that the ablation segment was or was notassociated with a complication such as damage to the esophagus, damageto the phrenic nerve, damage to the venous roots, and/or arisk-associated event during ablation such as charring or “steam pop”.Steam pop is a term for a condition wherein rapid expansion of steamduring an ablation creates an audible “pop”; this is associated in theliterature with a risk of complications such as heart wall perforation.

In some embodiments, data indicating observed ablation segmenteffectiveness 2102 may be obtained by the same catheter that was used toform the ablation. For example, a probe of an RF ablation catheter isoperated to ablate tissue, and then electrodes of the same catheter areused to measure potential electrical fields induced in the vicinity ofthe ablation. From these measurements, impedance properties indicatingablation segment and/or sub-lesion state may be calculated. In someembodiments, the impedance properties in turn indicate dielectricproperties of tissue that are changed as a result of tissue ablation.Dielectric properties and/or impedance are optionally interpreted asindicating local tissue state and in particular, local tissue state(s)as being permanently ablated (e.g., converted to fibrotic tissue),edematous but not fibrotic, and/or healthy. Data provided as acollection of observed ablation segment effectiveness data 2102 isoptionally expressed in any suitable format; for example: voltages,impedances, dielectric parameters, and/or tissue state(s) inferredtherefrom. Optionally, another measure of observed ablation segmenteffectiveness is provided, for example, measurements of electricalisolation (e.g., lack of impulse conduction across the ablationsegment), or clinical observation that disease is absent. In someembodiments dynamics of measurements such as impedance measurements areused. For example, impedance at an ablation region is optionally set toa baseline before ablation begins (e.g., as if the tissue is healthy).During and/or after ablation, changes in impedance (drops in impedance,for example) are measured (e.g., at frequencies up to about 1 MHz), andthese changes optionally serve as input to an ablation segmenteffectiveness estimator. The relevant dynamics are optionally providedin terms of magnitude, rate (slope and/or exponent, for example), and/orin terms of function shape, for example, linear, exponential,logarithmic or another shape.

In some embodiments, the impedance measurements include measurements ofimpedance between different electrodes on a prove of the ablationcatheter (e.g., between a tip electrode of the ablation catheter andanother electrode on the same catheter), between one or more electrodeson the ablation catheter and one or more electrodes on another catheter,and/or between one of more of the ablation electrodes and one or morebody surface electrodes. In some embodiments, the impedance measurementsinclude measurement of impedances at various frequencies, e.g., fromabout 10 kHz to about 1 MHz.

Optionally, estimates of ablation segment effectiveness 2106 areaccompanied by a metric of certainty, for example, sensitivity and/orselectivity. Estimated ablation segment effectiveness 2106 collectedalong a whole ablation line optionally are configured to identify themost likely location or locations along an ablation line whereincomplete electrical isolation occurs or will occur. In someembodiments, estimated ablation segment effectiveness 2106 is displayedwithin an interactive user interface facilitating use by an operator toexamine the estimate of ablation segment effectiveness from differentperspectives: for example, to check certainty of results, to checkestimated ablation segment effectiveness 2106 corresponding to estimatedposition of failed electrical isolation along the ablation line, tocompare the current ablation segment with other ablation segments in adatabase which historically have produced similar ablation segmenteffectiveness estimator results, etc.

Model Input to Machine Learning, and Pre-Processing of Inputs

In some embodiments, the machine learning of block 2103 proceeds on thebasis of an assumed ablation segment model 2101. Optionally, ablationsegment model 2101 is naïvely structured. For example, it optionallysimply hypothesizes for the machine learning of block 2103 a correlation(naïve as to underlying structure) between the prior inputs of ablationsegment effectiveness parameters 2100 and the feedback inputs ofobserved ablation segment effectiveness 2102.

However, it is a potential advantage to at least partially structureinputs based on known physical and/or causal relationships. Thestructure can be provided as part of ablation segment model 2101, and/orby pre-processing of data provided as inputs. For example, inputs fromthe ablation segment effectiveness parameters 2100 are optionallypre-processed to be expressed in terms of sub-lesion size (diameterand/or depth, for example). Additionally or alternatively, inputs arestructured by the ablation segment model itself into indications ofsub-lesion size (e.g., machine learning is applied to a model equationoperating on the ablation segment effectiveness parameters in one ormore combined terms, rather than only in terms each comprising adifferent raw ablation segment effectiveness parameters).

Pre-interpretation of raw inputs indicating ablation segmenteffectiveness (e.g., interpretation of electrical field measurementssuch as dielectric measurements to identify local tissue state) providesa potential advantage by reducing complexity of the data space (andpotentially noise in the data) before applying machine learning to it.However, there is a potential disadvantage in over-simplifying and/ordistorting data in observed ablation segment effectiveness 2102 bypre-interpreting, as this can reduce or destroy correlations latent inthe raw inputs.

Optionally, use of pre-processing enables creating ablation segmenteffectiveness estimators which are applicable to different types of rawinput. For example, part of the difference between simulations ofsub-lesion size (e.g., before ablation) and measurements of sub-lesionsize after ablation can be abstracted away by making sub-lesion sizeitself one of the inputs in the ablation segment effectivenessparameters 2100. That has the potential advantage of facilitatingcomparison of predictions from a planned procedure with predictions fromactual procedure results, for example.

In some embodiments, the raw measurements indicating ablation segmenteffectiveness (e.g., electrical field measurements such as dielectricmeasurements) may be used as ablation segment effectiveness parameters2100 (e.g., without any calculations) and may be input as training setdata.

Learned Ablation Segment Estimator

After the above-described inputs are suitably defined and received for aplurality of sub-lesions (e.g., 50, 100, 1000, or another larger,smaller, or intermediate number of sub-lesions), machine learning atblock 2103 may use one or more machine learning methods to produce alearned ablation segment effectiveness estimator 2104. Examples ofmachine learning methods used in some embodiments of the presentinvention include, for example: decision tree learning, association rulelearning, an artificial neural network, deep-learning artificial neuralnetwork, inductive logic programming, a support vector machine, clusteranalysis, Bayesian networks, reinforcement learning, representationlearning, similarity and metric learning, semi-supervised learning (forexample: multi instance learning) and/or another technique taken fromthe art of machine learning. In some embodiments, the estimator output(e.g., estimated segment effectiveness) may include a binary prediction(e.g., +1, −1). In some embodiments, the estimator output (e.g.,estimated segment effectiveness) may include a probabilistic outputbetween zero and one (e.g., a dynamic score). Such embodiments may beadvantageous when using an estimator during an ablation procedure. Forexample it may facilitate a user's decision whether to proceed orcomplete the procedure, and/or whether to adjust the ablation plan.

In some embodiments, the learned ablation segment effectivenessestimator 2104 comprises a set of learned weights applied to terms ofablation segment model 2101. Application of the ablation segmenteffectiveness estimate at block 2105 comprises plugging into the modelappropriate values from a collection of ablation segment effectivenessparameters 2100, and calculating the result. The result is produced asan estimated ablation segment effectiveness 2106. Other collections ofablation segment effectiveness parameters 2100 may refer to ablationsegment effectiveness parameters 2100 collected after learned ablationsegment effectiveness estimator 2104 was created, e.g., ablation segmenteffectiveness parameters 2100 collected in run-time during a medicalprocedure. Other collections of ablation segment effectivenessparameters 2100 may include dielectric measurements measured run-timeduring a medical procedure. Such dielectric measurements may be used asinputs to learned ablation segment effectiveness estimator application2105 to obtain estimates of ablation segment effectiveness 2106.

It should be noted that learned ablation segment effectiveness estimator2104 may be updated and/or adjusted (also referred to as refreshed)based on such other collections of ablation segment effectivenessparameters 2100. Estimates of ablation segment effectiveness 2106 may beused to adjust an ablation plan.

In some embodiments, the estimated ablation segment effectiveness 2106is expressed in the same terms as used by ablation segment model 2101for learning. Optionally, the estimated ablation segment effectiveness2106 is also accompanied by an estimate of the certainty of theprediction, for example, based on statistically determined specificityand/or sensitivity. Optionally, post-processing is applied to convertthe estimated ablation segment effectiveness 2106 into another form. Forexample, the feedback input in the observed ablation segmenteffectiveness 2102 may not itself encode a spatial extent of the regionelectrically isolated by a particular ablation segment. Rather, forexample, it may simply indicate that the electrical isolation issufficient to maintain disease prevention across the ablation segment.Since this implies that sub-lesions contributing to the ablation segmentare transmural, it is possible, optionally, to infer at a later stagethat the depths of these sub-lesions are about the same as the thicknessof the tissue. Similarly, the positions and sizes of adjoiningsub-lesions optionally provide post-processing constraints on sub-lesiondiameter within an ablation segment.

It should be understood that it is often possible to alternatively makesuch adjustments in a stage of pre-processing (rather thanpost-processing). For example evidence of “successful block” isoptionally converted to size constraints in the feedback inputindicating observed ablation segment effectiveness 2102. In someembodiments, constraints may be accounted for in the ablation segmentmodel 2101 itself; for example, by defining ablation segment model 2101to include a term comparing sub-lesion depth to atrial wall width.

Inputs to Ablation Segment Effectiveness Estimators

Reference is now made to FIG. 14B, which schematically represents arange of options for prior inputs to a ablation segment effectivenessestimator and/or for use in machine learning of the ablation segmenteffectiveness estimator, comprising parameters potentially relating toablation segment effectiveness, according to some embodiments of thepresent disclosure.

The number of possible separate inputs to a collection of ablationsegment effectiveness parameters, in some embodiments, is both large andoptionally variable. Sets of ablation segment effectiveness parameters2100 used, in some embodiments, and/or portions of such sets are alsoreferred to herein as “input data” The inputs listed in FIG. 14B may beviewed as providing a menu of examples of parameters, from which anysuitable subset is optionally used. Optionally, another parameter sourcenot shown among those in the FIG. 14B is used.

The broadly hierarchical (mostly branched, but in some places looping)arrangement by which optional inputs are shown in FIG. 14B is primarilyused as a guide to help organize the descriptions that follow. Terminalnodes (terms without borders) represent numerous types of basic inputs(e.g. measurements, settings, and outside data such as anatomical atlasdata). Nodes surrounded by thin cloud-shaped borders list intermediateorganizational concepts that link the basic inputs (and/or otherorganizational concepts) as alternative and/or complementarycontributions to the full collection of ablation segment effectivenessparameters 2100, as detailed for each. Optionally (as indicated in themore detailed descriptions following), some of the cloud-bordered nodesrepresent higher level abstractions used in the ablation segmenteffectiveness parameters in place of one or more basic inputs (e.g.,transformation of ablation parameters to an expected ablation state).With regard to the higher level abstractions described in particular, anablation segment effectiveness estimator may or may not receive suchinformation as part of ablation segment effectiveness parameters itreceives. Such abstractions can help in the generation and/or use of anablation segment effectiveness estimator for such reasons as providingprior accounting for variance and/or assisting in referencing estimatorresults to different types of raw input. However, they are oftenthemselves based on estimates and/or theoretical considerations whichthe actual may or may not fully support.

Next to the triangle that indicates the root of the hierarchy ofablation segment effectiveness parameters 2100 are three clouds withthicker borders. These define a basic conceptual division of theablation segment effectiveness parameters into three: (1) parametersindicating information about size and/or position of the ablationsegment itself (parameterized ablation segment 2230, which may be saidto be characterized by ablation segment parameterizing data), (2)parameters indicating information about the tissue environment in whichthe ablation segment is placed (tissue condition 2220), and (3)effectiveness criteria 2210, which define empirical, theoretical,clinical, and/or safety conditions within which an ablation procedure isoperating.

Apart from the organization just described, the parameters of FIG. 14Bbelong to one or more of three time stages:

-   -   Pre-ablation Inputs indicate, for example, results of planning        activities (ablation line definition, pre-ablation simulations        of sub-lesion extent, effects on ablation segment of different        spacings, etc.).    -   Intra-Lesioning Inputs include, for example, measurements taken        and operational parameters used during an ablation procedure        (optionally including parameters measured immediately after        ablation but before the overall procedure ends).    -   Post-Lesioning Inputs include, for example, clinical        observations in the period after the procedure ends (day to        years), post-procedure imaging, and/or follow-up catheterization        procedures.

For some parameters, stages are explicitly mentioned in FIG. 14B (e.g.,as pre-ablation simulation 2240, intra-ablation parameters 2261, andpost-ablation observations 2232). Staging relevant to other inputs ismentioned where those inputs are described.

According to some embodiments, estimating effectiveness of ablation toform an ablation segment comprises receiving data; wherein the receiveddata comprises lesion data on two or more sub-lesions forming theablation segment and data on interaction between the two or moresub-lesions, and estimating an effectiveness of the ablation segment ina tissue; wherein the estimating is based on use by computer circuitryof an estimator constructed based on observed associations betweenpreviously analyzed received data, and observed ablation segmenteffectiveness, the estimator being applied to the received data.

In some embodiments, the effectiveness of the ablation segment comprisesthe joint effectiveness of the two or more sub-lesions in preventingmyocardial transmission through locations of the sub-lesions themselves,and therebetween. In the case of sub-lesions formed by dragging anablation probe across tissue, estimating effectiveness of ablation toform an ablation segment comprises receiving data; wherein the receiveddata comprises lesion data on two or more sub-lesions forming theablation segment, data on interaction between the two or moresub-lesions and data on ablated tissue properties and/or contact forceapplied during the ablation, and estimating an effectiveness of theablation segment in a tissue; wherein the estimating is based on use bycomputer circuitry of an estimator constructed based on observedassociations between previously analyzed received data, and observedablation segment effectiveness, the estimator being applied to thereceived data. In some embodiments, the effectiveness of the ablationsegment comprises the joint effectiveness of the two or more sub-lesionsin preventing myocardial transmission through locations of thesub-lesions themselves, and therebetween.

In the case of sub-lesions formed by point-by-point ablation, estimatingeffectiveness of ablation to form an ablation segment comprises, in someembodiments, receiving data; wherein the received data comprises lesiondata on two or more sub-lesions forming the ablation segment, data oninteraction between the two or more sub-lesions and data on ablatedtissue properties; and estimating an effectiveness of the ablationsegment in a tissue; wherein the estimating is based on use by computercircuitry of an estimator constructed based on observed associationsbetween previously analyzed received data, and observed ablation segmenteffectiveness, the estimator being applied to the received data. In someembodiments, the effectiveness of the ablation segment comprises thejoint effectiveness of the two or more sub-lesions in preventingmyocardial transmission through locations of the sub-lesions themselves,and therebetween.

According to some embodiments, data on interaction between the two ormore sub-lesions may include distance between the two or moresub-lesions and/or timing between two or more sub-lesions.

In some embodiments, interaction between the two or more sub-lesions maybe defined as a non-linear function of a plurality of parameters, e.g.,lesion data on two or more sub-lesions forming the ablation segment. Insome embodiments, interaction between the two or more sub-lesions may bedefined as a non-linear function of sub-lesion characterizationparameters having different values among the plurality of sub-lesions.

According to some embodiments, estimating effectiveness of ablation toform an ablation segment comprises receiving data; wherein the receiveddata comprises lesion data on a first sub-lesion, lesion data on asecond sub-lesion, and distance between the first sub-lesion and thesecond sub-lesion; and estimating an effectiveness of the ablationsegment in a tissue; wherein the estimating is based on use by computercircuitry of an estimator constructed based on observed associationsbetween previously analyzed received data, and observed ablation segmenteffectiveness, the estimator being applied to the received data.According to some embodiments, the received data comprises timingbetween lesioning of the first sub-lesion and the second sub-lesion. Insome embodiments, the effectiveness of the ablation segment comprisesthe joint effectiveness of the two or more sub-lesions in preventingmyocardial transmission through locations of the sub-lesions themselves,and therebetween.

Ablation Segment Parameterization

Starting now from the node for parameterized ablation segment 2230(“segment” is again used in the figure as a shorthand for “ablationsegment”): In some embodiments, the ablation segment is parameterizedbased on one or more of pre-ablation simulations 2240, post-ablationobservations 2232, inferred ablation state 2260 after ablation (e.g., ahigh-level description of sub-lesion positions and sizes which isoptionally inferred from measurements and/or activities), and/or any ofthe other parameters which are shown feeding into these nodes. Inferredablation state 2260 may include one or more states inferred fromdielectric measurements and/or any manipulation (such as mathematicalmanipulation) on such dielectric measurements). Inferred ablation state2260 may include one or more results inferred from dielectricmeasurements by analytical calculations and/or machine learningtechniques.

In some embodiments, the ablation segment and/or its sub-lesions is/areparameterized non-geometrically in the collection of ablation segmenteffectiveness parameters 2100 supplied to an ablation segmenteffectiveness estimator. For example, a dielectric measurement 2233optionally provides information about how much local tissue in theregion of a measurement electrode has been ablated, without necessarilyproviding information about how the ablation segment and/or itssub-lesions is shaped. Potentially, a non-geometrical parameterizationmay still provide enough information for machine learning to converge onan estimator linking the ablation segment parameterization to anestimated ablation segment effectiveness.

Additionally or alternatively, in some embodiments, the ablation segmentand/or its sub-lesions is/are parameterized as a geometrical objecthaving a defined shape. An ablation segment is optionally parameterizedby one or more distances between a plurality of sub-lesions (whichpotentially affects the appearance of transmission gaps), and/or by atime elapsed between creation of the sub-lesions. Optionally elapsedtime is measured between initiations of ablations, terminations ofablations, termination of one ablation and initiation of another, or anyother suitable definition of elapsed time. Elapsed time could affectablation segment shape due to merging of sub-lesions affected byresidual temperature increases; and/or due to changes in edematousstate, for example due to recent nearby ablations.

Optionally, a geometrical parameterization of sub-lesion shape definesat least a sub-lesion depth and a proximal diameter (diameter of asub-lesion at the side directly in contact with the ablation device).Optionally, these parameters are incorporated into a definition of ageometrical solid representing the shape of the sub-lesion; for example,a frustum of a paraboloid, ellipsoid, cone, or another shape.Parameterization of ablation segment and/or its sub-lesions asgeometrical shapes, though potentially subject to estimation errors ofits own, has the potential advantage of being interconvertable betweenoutputs of pre-ablation simulation 2240, inferred ablation results 2260(and/or intra-ablation parameters 2261), and/or post-ablationobservations 2232.

Optionally, such interconvertibility allows the same estimator to beused on data from different original sources. Additionally oralternatively, a non-spatial “common parameterization” is used. Forexample, a spatial definition of an ablation segment and/or itssub-lesions determined by simulation is optionally re-parameterized intothe results of simulated “measurements” that would be expected from it.Optionally, re-parameterization is used to convert ablation segmenteffectiveness parameters size measurements known from in vitro studiesinto ablation segment effectiveness parameters more convenient fordirect in vivo use, and/or conversely.

Each of the three main ablation segment parameterizing data stages isnow discussed in turn.

In some embodiments, pre-ablation simulations 2240 produce parameterizeddescriptions of ablation segments (and/or their sub-lesions) which areplanned to be formed during an ablation procedure. The pre-ablationsimulations 2240 are based on an ablation segment ablation plan 2241describing where and how ablations are to be made. Optionally thesimulation results are further modulated by relevant parameters oftissue condition 2220 (e.g. tissue thickness 2245, state of edema 2250,tissue type(s) 2221, fiber orientation 2225, and/or intra-ablationtemperature 2255; each is further described below). Optionally, thepre-ablation simulation 2240 is produced as part of creating theablation segment ablation plan 2241 in the first place. Tissue thickness2245 may be calculated and/or inferred (e.g., by machine learningmethods) from dielectric measurements 2246.

Insofar as the pre-ablation simulation 2240 optionally comprisesspatially defined simulations such as EM and/or thermal simulation, itis relatively straightforward, in some embodiments, to define theparameterized ablation segment 2230 in terms of geometrical shape.

In some embodiments, parameterized ablation segment 2230 is based oninferred ablation results 2260. Similarly as for pre-ablationsimulations 2240, the inferred ablation results estimate the effects ofparameters used during ablation, optionally modulated by the parametersof tissue condition 2220. However, instead of choosing ablationparameters from an ablation plan 2241 (e.g., as indicated by theconnection in FIG. 14B shown between segment ablation plan 2241 andintra-ablation parameters 2261), the intra-ablation parameters 2261 usedin actual ablation are used. Examples of intra-ablation parameters 2261include ablation probe contact 2265, ablation power 2263, ablationtiming 2264, and optionally other ablation parameters not shown, such asphase and/or frequency.

In some embodiments, contact 2265 is measured and/or estimated based onforce measurements 2266 (e.g., measurements by one or more force sensorson the ablation probe), dielectrically-measured quality of contact 2268,and/or angle 2267 of probe contact with tissue, which can be measured,for example, by comparing readings from a plurality of force sensors,and/or based on indications of dielectrically measured quality ofcontact. Dielectric measurement of contact quality is described, forexample in International Patent Application No. PCT/IB2016/052686.Optionally, any combination of one or more of the parameters used and/ormeasured during ablation are used directly by an ablation segmenteffectiveness estimator, optionally without the prior interpretationrepresented by inferred ablation results 2260.

In some embodiments, dynamics of contact (under any suitable measure ofcontact) is provided as an ablation segment effectiveness estimatorinput. As heart tissue moves (e.g., in synchrony with the heartbeatand/or respiratory cycles), an ablation probe in contact with tissue maymove relative to the tissue. With lowered relative motion dynamics, insome embodiments, an ablation segment effectiveness estimator ispotentially more likely to predict a higher effectiveness of an ablationsegment. In some embodiments (e.g., for ablation segment effectivenessestimation during pre-planning), a prediction of relative motiondynamics is made for a particular heart region based on experience ofthis parameter in different heart regions. Optionally, a simulation ofheart motion is used to predict relative ablation probe/cardiac tissuemotion dynamics, e.g., under contact pressures typical for ablationprocedures.

Also comprising ablation segment effectiveness parameters 2100, in someembodiments, are measured positions 2237 of the probe, which can bemeasured, for example, based on electromagnetic field-guided navigation2238, dielectrically-guided navigation 2239B, and/or imaging-basednavigation 2239A. In some embodiments, the probe is moved to be placedat measured positions 2237 based on indications in segment ablation plan2241 (e.g., as indicated by the connection in FIG. 14B shown betweensegment ablation plan 2241 and positions 2237).

In some embodiments, post-ablation observations 2232 are made. Thepost-ablation observations 2232 optionally include one or moredielectric measurements 2233 characterizing tissue in the region of theablation segment and/or sub-lesion, measurements of acute electricalisolation 2234 (that is, measurements made within the time of theinitial ablation procedure), measurements of persistent electricalisolation 2236 (that is, measurements and/or clinical observations madein the days, months, and/or years after the initial ablation procedure),and/or measurements made by imaging 2235, for example, nuclear medicineimaging of fibrotic extent. Use of dielectric measurements forcharacterizing ablated/lesioned and/or other tissue states such asedema, is described, for example, in International Patent ApplicationNos. PCT/IB2016/052690 and PCT/IB2016/052686. In some embodiments, anintracardiac electrogram is measured in the region of an ablationsegment and used as an input to an ablation segment effectivenessestimator. Dynamics of the electrogram during and/or after lesioning arepotentially indicative of likely ablation segment effectiveness,according to an amount of amplitude decay from a base amplitudepre-ablation (more decay being generally associated with greatereffectiveness).

Optionally, an ablation procedure is completed by making post-ablationobservations at positions near ablation segments and/or along theablation line which the ablation segments define. In some embodiments,the post-ablation observation positions are suggested automatically, forexample, to check on tissue state at the locations between sub-lesionsmost at risk for allowing electrical reconnection (optionally, this riskis itself determined by use of the ablation segment effectivenessestimator), and/or to check the sub-lesions where there was someindication of problems during the ablation procedure itself.

Tissue Condition Influences on Ablation Segment Effectiveness

Turning now to tissue conditions 2220, several main relevant conditionswere listed already above in relation to pre-ablation simulations 2240,and inferred ablation state 2260. In some embodiments, at least some ofthe tissue conditions 2220 are provided as part of the ablation segmenteffectiveness parameters 2100. For example, a sub-lesion partiallycharacterized in the ablation segment effectiveness parameters by itsdepth may be transmural or not (and thus electrically isolating or not)depending on the thickness 2245 of the tissue ablated. Similarly, anablation segment may more or less isolating depending on the localorientation 2225 of myocardial fibers.

In contrast, some tissue conditions shown are effectively accounted for,in some embodiments, by the parameterization of the ablation segment2230. These can be optionally be left out of the ablation segmenteffectiveness parameters based on which machine learning and/oreffectiveness estimation are performed. For example, temperaturemeasured during ablation may affect how an ablation segment isparameterized, but leave little residual effect affecting the estimationof ablation segment effectiveness.

In some embodiments, tissue conditions are provided as input to anablation segment effectiveness estimator indirectly, for example, as anindication of a region (e.g., a cardiac region) where an ablation isplaced. Effects associated with a particular region are optionally dueto a number of more specific effects (e.g., typical thickness, fiberorientation, thermal properties, etc.) which may or may not beexplicitly available as inputs. The use of machine learning potentiallyallows accounting for such effects implicitly, through associatedposition information.

Tissue thickness 2245 is a factor partially governing transmurality.Thicker tissue potentially requires deeper ablation in order to achieveeffective electrical isolation. Thickness is optionally characterizedbased on tissue atlas information 2248, and/or based on imagingmeasurements 2247 obtained by analysis of anatomical images of theindividual patient (obtained, e.g., by MRI, CT, nuclear medicine, oranother method).

Intra-ablation temperature 2255 is optionally simulated in pre-ablationsimulations 2240, inferred from actual ablation parameters as part ofinferred ablation results 2260, and/or measured 2256 during ablation.With RF ablation, for example, temperature can be an indication ofablation progress. In some embodiments dynamics of temperature serve asan input. For example, temperature measured at an ablation probe tipwhich rises and maintains stability during ablation is potentially anindication of successful ablation, while a temperature which drops ispotentially and indication of less likely success.

Similarly, edema may be simulated, inferred, or dielectrically measured2251. Edema in cardiac tissue is potentially elicited by nearbyablations, or even by rough contact with a probe. Edematous tissue ispotentially more resistant to effective ablation: for example, edematousfluid can thicken tissue so that sub-lesion transmurality is harder toachieve.

Tissue type 2221 is optionally characterized (typically before aprocedure) by one or more imaging modalities 2222. Tissue type 2221 canalso be characterized intra-procedure by dielectric mapping 2223 (and/orimpedance mapping), for example as described in relation to FIG. 4A.

In some embodiments, impedance mapping (optionally omitting conversionto a dielectric map) itself is performed at one or more frequencies,e.g., frequencies up to about 1 MHz. Impedance maps, and/or maps ofimpedance dynamics, optionally provide indications of (e.g., correlatewith) one or more of the following:

-   -   Initial wall composition (e.g., living or fibrotic);    -   Heat conduction of neighboring organs (e.g., lung is less heat        conducting);    -   Contact quality;    -   Wall thickness;    -   Tissue temperature;    -   Wall tissue decomposition (e.g., upon ablation);    -   Contact quality stability;

Several of these uses of impedance are described in connection toparticular elements of FIG. 14B. It is noted that impedance measurementsof these parameters are potentially at least somewhat entangled,statistically. However, correlations with other inputs, for example,other measurements, past observations, particular conditions of themeasurement (e.g., force, position, current operations), etc.potentially assist in separation. It is noted that attribution ofmeasured impedance characteristics to particular causes is notnecessarily required and/or performed by an ablation segmenteffectiveness estimator, insofar as machine learning can operate onassociations between observations without necessarily using and/orforming specific attributions of measurements to causes.

In some embodiments of the invention, the level of spatial resolution ofadjacent features provided by the dielectric mapping 2223 is betweenabout 0.1 and 1 mm. Optionally, the spatial resolution is about 0.1 mm,about 0.5 mm, 1 mm, 2 mm, 5 mm, or another larger, smaller, and/orintermediate value. In some embodiments, the map is made available fordisplay to an operator.

Fiber orientation 2225 is discussed in detail herein in relation toFIGS. 5A-5B. The effectiveness of an ablation segment for creatingelectrical isolation is potentially affected by the direction ofmyocardial fibers running nearby: for example, fibers oriented to runthrough a gap (for example, as in FIG. 5B) apparently transmit impulsesmore efficiently than fibers oriented so that impulses must jumplaterally from fiber to fiber to pass the gap (for example, as in FIG.5A). Optionally, fiber orientation used in simulating or inferringablation effects is derived from atlas-based assumptions 2227 aboutheart wall anatomy. Additionally or alternatively, fiber orientation isimaged 2226, for example, using echocardiography-based shear waveimaging, and/or diffusion tensor imaging. In some embodiments, fiberorientation between two points is inferred from the rate of impulsetransmission 2228 therebetween. Optionally superficial fiber orientationis indicated by differences in contact indicated by impedancemeasurements made as an electrode travels, for example, along (in thedirection of) or across (at least partially orthogonal to) a directionof fiber orientation.

In some embodiments of the invention, patient data parameters (alsoreferred to herein as “patient data”) are also provided as part ofablation segment effectiveness parameters 2100.

Optionally, any of the above, or another type of patient data, are useddirectly as inputs to machine learning. In some embodiments, factorswhich are particularly associated with risk (gender, left atrialappendicular morphology, alcohol consumption, obesity, hypertension,obstructive sleep apnea, age, etc.), and optionally still moreparticularly risk of mortality or morbidity as a result of having tore-perform a procedure, are used to set a threshold of acceptableablation segment effectiveness. For example, during ablation planadjustment, an automatic algorithm is optionally set to be moreaggressive in indicating a potentially ineffective ablation segment,and/or more aggressive in making suggestions for mitigation ofpotentially ineffective ablation segments. In some embodiments,determination of elevated risk (or another patient-specific input)optionally leads to a suggestion to follow a qualitatively differentplan: for example, to ablate along a different ablation path.

In another example: during ablation, an automatic algorithm estimatesexpected clinical outcome for a plurality of parameter sets of furtherablation, e.g., expected outcome if the next ablation site is placed ata plurality of alternative positions, and/or if the next ablation begins(or potentially does not) within a given time period. The estimations ofthe clinical outcomes for different parameter sets may be displayed tothe physician, who may choose between them. The physician's choice maybe based on considerations such as the difference in the expectedoutcome and the difference in the difficulty of obtaining the requiredablation parameters in a set. For example, if ablating at region that isdifficult to reach with the catheter from its current position bringsonly slightly better results than ablating at a second region that iseasier to reach, the physician may choose the second region.

Optionally, data indicating therapeutic strategy and/or events (asdescribed above) are included in the ablation segment effectivenessparameters 2100.

Effectiveness Criteria

In some embodiments, effectiveness criteria 2210 are incorporated to theablation segment effectiveness parameters. Optionally effectivenesscriteria 2210 comprise criteria defining limits on what features of anablation segment are expected to be needed in order for it to be aneffective ablation segment. These can include, for example, descriptionsof sub-lesion/tissue parameter pairings that are expected, based ontheoretical and/or empirical data to allow and/or prevent effectivetransmurality of block (isolation-effective transmurality 2212). In someembodiments, ablation segment location 2214 itself is considered part ofthe ablation segment effectiveness parameters, since ablation segmentsshould be positioned where they interfere with impulse transmission.Overall distribution of ablation segments will usually fit one of asmall number of general profiles, such as encircling of pulmonary veinswith one large loop or with a plurality of smaller loops.

Also optionally defined as part of the ablation segment effectivenessparameters is an empirical and/or theoretical model of how closeinter-sub-lesion gaps defining ablation segments should be in order toproduce effective block (isolation-effective sub-lesion spacing 2211). Adistinction may be made between embodiments in which an ablation segmenteffectiveness estimator is at least initially uninformed as to thisparameter, and embodiments of ablation segment effectiveness estimatorswhich use it. An initially uniformed, machine-learned ablation segmenteffectiveness estimator potentially arrives at explicit or implicitestimation of what spacings between sub-lesions are effective byreference to the data of its training set, without any prior conditionson what the spacing should be. However, the learning of an informedmachine-learned ablation segment effectiveness estimator is potentially“helped along” by such prior data; for example, for example, because apotentially significant part of the training set variance is alreadyexplained and/or attributed.

Optionally, effectiveness criteria 2210 relate to regions where ablationshould be avoided (avoided regions 2213). These places optionallyinclude, for example, sub-lesions located too near to the esophagus, toonear the phrenic nerve, and/or too near the venous roots. Constraintssuch as avoided region 2213 optionally provide a potential advantage,e.g. for indicating whether or not an ablation plan is safe, and/or forindicating a potential for complications, in case the ablation procedurehas already been performed. Additionally or alternatively, other safetyconstraints are used; for example, constraints on maximum powersettings, maximum duration of ablation, etc., which, if exceeded, couldindicate an increased potential for complications during and/or after aprocedure.

Application and Dynamic Adaptation of an Ablation Plan

Reference is now made to FIG. 12 , which schematically illustrates amethod of real-time use, with optional adjustment, of an ablation plan,in accordance with some exemplary embodiments of the invention.Reference is also made to FIG. 13A, which illustrates the 3-D display ofa planned lesion ablation line for a left atrium 800, along with anablation probe 10, in accordance with some exemplary embodiments of theinvention. Reference is further made to FIG. 13B, which illustrates aninterior 3-D view of left atrium 800, probe 10, and planned ablationline 802, in accordance with some exemplary embodiments of theinvention.

In some embodiments, a line of planned ablation 802, together withparameters of planned sub-lesions may be used during a procedure bycombining measured ablation probe positions within the body with cues toguide operation of the ablation probe so that the previously determinedablation plan is followed.

The flowchart begins (after production or receiving of an ablationplan). The ablation plan may be provided by a lesion planning system(for example as described in International Patent Application No.PCT/IB2016/052688, the contents of which are included by referenceherein in their entirety) or may be provided by the user (e.g., thephysician).

At block 130, in some embodiments, a portion of a planned lesion is made(e.g., a sub-lesion comprising ablation from a fixed ablation probelocation, or a dragged-out portion of a lesion). A planned lesion mayinclude one or more planned ablation segments. Optionally, the lesion ismade in conjunction with visual guidance provided to the user, forexample, visual guidance as shown in FIGS. 13A-13B. In FIG. 8A, visualguidance is presented from an outside-the-heart point of view. In FIG.13B, the point of view is that of a probe of the ablation catheteritself, shown as if from within a heart chamber emptied of blood.Optionally, as the actual ablation probe is moved, its motions(measured, for example, by system 1100 of FIG. 1A) are shown also in alive presentation of the views of FIG. 13A and/or FIG. 13B. In someembodiments, the display is adjusted to also include anatomicallyrealistic tissue coloring and/or responsiveness to ablation probecontact and/or to the effects of ablation itself.

Optionally, selection of pre-planned ablation parameters isautomatically made when an ablation probe approaches the next plannedlesion position. Optionally, the system guides the user to the nextplanned lesion position. Optionally, a user is provided with aninterface which allows modifying or overriding these settings.

In some embodiments, the system adapts the ablation plan to actualevents during ablation, for example, as now described in relation toblocks 132, 134, 136, 138, and 140. In some embodiments, the systemadapts the ablation plan in response to one or more estimations ofablation line effectiveness, for example: lesion effectiveness, ablationline effectiveness, and/or ablation segment effectiveness. Estimatedablation line effectiveness may be obtained from one or more estimators(for example: as described in FIG. 6 ).

At block 132, in some embodiments, the system 1100 characterizesparameters such as ablation probe position and settings of the actualablation operation performed (optionally, the ablation operation set tobe performed based on the current ablation probe position and settings).Optionally, information about the ablation probe position includes acontact force or other assessment of contact quality (e.g. dielectricproperty contact quality assessment) between the ablation probe andtarget tissue. Optionally, the new state of tissue in the lesionedregion is modeled, based on actual ablation position and parameters, andon data previously configured for thermal simulation. Additionally oralternatively, at block 134, in some embodiments, the lesion actuallycreated is itself characterized, for example, by the analysis ofdielectric measurements and/or temperature readings. In someembodiments, at block 134, lesion effectiveness, ablation lineeffectiveness, and/or ablation segment effectiveness are estimated.

In some embodiments, block 134 may include application of a segmenteffectiveness estimator, lesion estimator, ablation line estimator,using any suitable inputs available from blocks 132 and 134, and/or frompreviously available data; for example as described in relation to block2105 of FIG. 14A, block 1605 of FIG. 1B or 1805 of FIG. 6 .

At block 136, in some embodiments, a determination is made as to whetheror not the plan is still being followed as currently defined and/orwhether the lesion is effective. In some embodiments, block 136 mayinclude a determination as to whether or not the resulting estimatedablation segment effectiveness (corresponding, in some embodiments, toblock 2106 of FIG. 14A) indicates that an effective ablation segment hasbeen formed (e.g., by placement of the most recent sub-lesion), and/orwill be formed if the next sub-lesion is created as already planned. Anygiven sub-lesion optionally both completes a previous ablation segmentand begins a new sub-lesion, so there may be one ablation segmenteffectiveness evaluation based largely on parameters describing and/ormeasuring what has happened already, and another ablation segmenteffectiveness evaluation which includes assumptions about future(planned) ablations.

If the plan is to remain unchanged, flow continues at block 140. Forexample, if the ablation segment effectiveness estimator resultindicates an effective ablation segment is being and/or has beenproduced, the flowchart continues with block 140.

Otherwise, at block 138, in some embodiments, the ablation plan isadjusted. Adjustment may be made in any parameter of the ablation planto adjust, for example: to a deviation from the previously plannedtiming and/or placement of sub-lesions, to a deviation from an expectedeffect of a lesioning operation (as measured, for example, fromdielectric measurements of lesion extent), and/or for a deviation froman expected pre-lesion tissue state (for example, an expectedpre-existing lesion is found to be of a different extent; measured, forexample, by dielectric measurements and/or measurements to assessfunctional blockage of impulse transmission).

In some embodiments, the adjustment is selected so that the ablationsegment effectiveness estimator estimates that the new resultingablation segment will be effective. Underlying reasons for a currentlypoor ablation segment effectiveness estimator result potentiallyinclude, for example:

-   -   Deviation from the previously planned timing and/or placement of        sub-lesions;    -   Deviation from an expected effect of an ablation operation (as        measured, for example, from dielectric measurements of        sub-lesion extent); and/or    -   Deviation from an expected pre-ablation tissue state (for        example, an expected pre-existing lesion is found to be of a        different extent).

The ablation segment effectiveness estimator itself does not necessarilyreport reasons for a poor effectiveness estimate. Nevertheless, in someembodiments this is apparent from one or more of the ablation segmenteffectiveness estimator inputs (particularly if deviating from a plannedresult), and/or from the types of planned adjustments which restore amore confident ablation segment effectiveness estimate. Optionally,adjustment is based on inspection of the ablation segment effectivenessestimator inputs by a user.

In some embodiments, the ablation segment effectiveness estimator itselfis used to automatically generate a suggested ablation plan alteration,for example by trying several different ablation plan adjustment options(for example using an effectiveness maximization search and/or anexhaustive search of effectiveness expected from key parameter changes),and presenting one or more of the best scoring ones for a user to selectfrom. In some embodiments, the ablation line estimator and/or lesionestimator may be used to automatically generate a suggested ablationplan alteration.

In some embodiments, adjustment may include generating plurality ofablation plans, and selecting one of the plurality of ablation plans foruse. For example, the user may select one of the plurality of plans,e.g., based on estimated indication of effectiveness.

For example, if a sub-lesion was placed with too large a gap between itand an adjacent sub-region, the plan may be adjusted to fill in the gapregion. In another example, if more time has passed between sub-lesionsthan the current plan anticipates (such that there has been too muchcooling in the interim), the recommended placement of the nextsub-lesion is brought closer to the previous lesion. For example, if asub-lesion was estimated to be non-effective, the plan may be adjustedto re-ablate, optionally at a higher power level and/or for a longerduration.

At block 140, in some embodiments, a determination is made as to whetheror not the ablation plan has been adequately completed (e.g., accordingto completion of the planned sequence of steps, and/or based onverification measurements of the actual lesion). A determination as towhether or not the ablation plan has been adequately completed may bebased on one or more of lesion effectiveness, ablation lineeffectiveness, and/or ablation segment effectiveness. For example, if anablation line estimator (e.g., estimator 1804) estimates that the lineis not effective, the flowchart returns to block 130 or block 138.

If not, the flowchart returns to block 130. Otherwise, the flowchartends.

The flowchart is optionally re-entered as many times as necessary tocomplete an overall ablation line.

Visualizations Used with an Ablation Segment Effectiveness Estimator

Reference is now made to FIG. 15A-15B, which schematically represents avisualization of results of an ablation segment effectiveness estimatorapplied to a plurality of ablation segment used in forming ablationlines within a left atrium, according to some embodiments of the presentdisclosure.

In some embodiments, the results are shown to a user and updated in realtime as new ablation segments are formed. Green spheres (lightest grayspheres) represent sub-lesions and their sizes; segments drawn betweenthese spheres represent ablation segments estimated to be effective.Blue spheres (darker spheres of same size as the lightest gray spheres,shown in FIG. 15B) represent currently planned sub-lesions in accordancewith an ablation plan. Presenting both planned sub-lesions and actualsub-lesions may allow the user (physician) to better track the ablationprocedure. An ablation probe is also shown in FIG. 15B. Small darkspheres anchor labels of anatomical landmarks such as the right inferiorpulmonary vein (RIPS), right superior pulmonary vein (RSPV), atrialappendage (AA), and another pulmonary vein (PV).

In some embodiments, the ablation segments are displayed on an“unwrapped” and/or flattened 3-D display (for example: as shown in FIG.15A) that allows simultaneous viewing of a large region of the heartchamber being treated. In FIG. 15A, the ring of light (green in color)circles represent ablations, and the dark line extending through a gapin the circles above the middle right of the right-hand ring of lightcircles comprises an indication that the ablation circle is estimated toremain incomplete.

There is no particular limitation of indication of estimator results tovisual presentation. For example, in some embodiments, segment estimatorresults (or joint effectiveness estimations) are presented as a sound(for example, a characteristic tone or sequence of tones representingestimated effectiveness at the end of each ablation operation to producea sub-lesion). Optionally, haptic feedback (e.g., actuation of aweighted spinner) indicates that an effective ablation segment isestimated to have been completed (or not).

In some embodiments, the indication is additionally or alternativelyprovided as an indication of whether or not (and/or where) to performadditional lesion-forming operations as necessary to increase theestimated effectiveness of the lesion segment.

In some embodiments, a method of indicating the effectiveness of anablation segment comprises receiving an estimate of how effective theablation segment is at preventing myocardial impulse transmissionthrough a region including and between a plurality of mutually adjacentsub-lesions; and providing an indication of the estimate which may beone or both of a direct indication that the ablation segment iseffective, and an indication of further action to potentially increasethe effectiveness of the ablation segment. Optionally, an ablationsegment estimated as being effective (e.g., estimated as preventing,and/or likely to prevent myocardial impulse transmission throughlocations of the sub-lesions themselves, and therebetween) is indicated(e.g., visualized) as a line connecting the sub-lesions. Optionally,ablation segment estimated as being not-effective (e.g., estimated asnot preventing, and/or unlikely to prevent myocardial impulsetransmission through locations of the sub-lesions themselves, andtherebetween) is indicated (e.g., visualized) differently than ablationsegment estimated as being effective. In some embodiments, theeffectiveness of each sub-lesion is indicated as well.

In some embodiments, the effectiveness of an ablation segment indicatedrepresents an estimation of future effectiveness, e.g., at a periodlater than the time of estimation and/or the time of ablation by atleast 1 month, 3 months, 6 months, 1 year, 2 years, 5 years, or anotherperiod.

In some embodiments, the estimate is affected by a plurality ofparameters in combination, such that for at least some combinations ofvalues of those parameters (e.g., over at least some range of values ofat least one of the parameters), no single parameter is itselfsufficient to yield the value of the estimate. In some embodiments, theparameters of the plurality of parameters includes one or more of howthe lesions are planned to be formed or actually formed by ablatingoperations (e.g., one or more of their planned locations, ablationparameters, tissue environment, and relative timing), and parameters ofthe sub-lesions themselves (e.g., one or more of the measured size,transmurality and/or tissue states of the sub-lesions, during and/orafter operations to form the sub-lesions).

In some embodiments, a method of indicating the joint effectiveness of aplurality of tissue-ablating operations comprises receiving an estimateof how effective the tissue-ablating operations is at preventingmyocardial impulse transmission through a region including and between aplurality of mutually adjacent sub-lesions; and providing an indicationof the estimate which may be one or both of a direct indication that thejoint effectiveness of the two or more sub-lesions is effective, and anindication of further action to potentially increase the jointeffectiveness of the two or more sub-lesions.

In some embodiments, a method of providing an indication of an estimatedjoint effectiveness comprises: receiving dielectric measurementsmeasured at a plurality of sub-lesions; estimating, the jointeffectiveness of the plurality of sub-lesions based on the dielectricmeasurements and providing the indication of estimated jointeffectiveness.

In some embodiments, the joint effectiveness indicated represents anestimation of future effectiveness, e.g., at a period later than thetime of estimation and/or the time of ablation by at least 1 month, 3months, 6 months, 1 year, 2 years, 5 years, or another period).

Reference is now made to FIG. 16 , which schematically representspairwise real-time lesion assessment based on use of an ablation segmenteffectiveness estimator, according to some embodiments of the presentdisclosure. In some embodiments, the images shown represent imagesdisplayed to a user in real time during an ablation procedure.

At left an ablation probe is shown navigating to a first sub-lesiontarget (e.g., the gray circle at right; blue if in color) in preparationfor ablation to form the sub-lesion. The current surface region pointedto by the ablation probe is optionally marked, for example, by a circle(circle at left, red in color). Optionally, when the two circles comeinto alignment with the ablation probe contacting tissue, the ablationprobe is optimally aligned for ablating.

At center, the ablation probe has ablated the sub-lesion, which is nowshown in a lighter, filled shading (green, if shown in color). A newtarget is shown by a new position of an open circle in the middle, andthe leftmost circle is again a pointed-at position. The ablation probeis again navigated to contact the region marked by the new open circle.During navigation, in some embodiments, a dotted line (or otherindication, not shown) optionally indicates that the ablation segmentwhich would potentially be formed from the indicated crossed circle isestimated to be ineffective (e.g., ineffective at creating impulseblock). Optionally, the line indication changes when the probe iswell-positioned to potentially create an effective block; e.g., itchanges to a solid line.

At right, the second lesion has been formed (now also shown in a lightercolor). If a connecting line is used, it would optionally remain, inthis example, because the ablation segment effectiveness estimator wouldestimate (based on the large separation) that the two sub-lesions whichnow together define an ablation segment are insufficient to form aneffective ablation segment. Optionally, the procedure continues bymitigating this situation, e.g., by further ablation directed to atleast one of the existing sub-lesion locations, and/or by creation ofone or more additional sub-lesions, e.g., an sub-lesion positionedbetween the first two sub-lesion.

Edema

Edema Time Course Simulation and Prediction

Reference is now made to FIG. 18 which is a schematic flowchart of amethod of deriving and applying an elicited edema estimator forpredicting edema, according to some embodiments of the presentdisclosure. First, inputs to and operation of machine learning at block2603 are described. Then application of a learned elicited edemaestimator 2604 at block 2605 is described (in FIG. 18 and other figuresherein, “edema estimator” is also used to refer to an elicited edemaestimator). Use is also made of the term “instance of edema”, whichrefers to an actual, predicted, and/or simulated (as appropriate to thecontext) edematous tissue reaction following some particular elicitingstimulus such as operation of an ablation modality and/or mechanicalcontact. Optionally there can be more than one instance of edemadeveloping in tissue at the same time, though elicited edema instanceswhich come to overlap in area and/or time are optionally treated asparts of a single edematous scenario.

Prior Inputs to Machine Learning of an Edema Estimator

In some embodiments, the block marked 2500 in FIG. 18 comprises aplurality of “collections” of elicited edema parameters 2500. Sets ofelicited edema parameters 2500 used, in some embodiments, and/orportions of such sets are also referred to herein as “input data” Eachsuch collection in turn comprises a heterogeneous set of data inputsrelating to one or more particular elicited edema instances. In someembodiments, the particular instance of edema is elicited (and/or isexpected to be elicited) by the operation of an ablation modality (e.g.,radio frequency ablation, cryoablation, microwave ablation, laserablation, irreversible electroporation, substance injection ablation,and high-intensity focused ultrasound ablation) acting on tissue whichis targeted for ablation or other medical treatment. In someembodiments, the instance of edema is elicited by mechanical contactbetween an intrabody probe (e.g. catheter probe) or other treatmentmodality and tissue.

The data inputs of a collection of elicited edema parameters 2500include, for example, sub-lesion measurements made during and/or aftersub-lesion ablation, placement of sub-lesions, ablation tool settingsgoverning sub-lesion formation, calculated results of ablation, and/ortissue conditions within which the instance of edema is situated. Insome embodiments, the data inputs comprise parameters describing force,and/or duration of mechanical contact.

Optionally, elicited edema parameters 2500 include other data; forexample: patient data parameter, patient clinical data, and/orpreviously acquired ablation and/or mechanical contact data for the samepatient. In some embodiments, parameters of elicited edema parameters2500 include any suitable parameter described in connection withablation segment effectiveness parameters 2100, for example, inreference to FIG. 14B herein and/or lesion effectiveness parameters1500, for example, in reference to FIG. 4B herein. In some embodiments,raw measurements indicating edema (e.g., electrical field measurementssuch as dielectric measurements) may be used as elicited edemaparameters 2500 (e.g., without any calculations) and may be input astraining set data to edema estimator 2604.

The inputs of each collection of elicited edema parameters 2500 at leastpotentially indicate (individually and/or in aggregate) informationabout the initiation and/or time course of a particular instance ofedema to which they relate.

At block 2603, in some embodiments, a plurality of collections ofelicited edema parameters 2500 may be provided as a training set for usein one or more machine learning methods, in order to generate a learnedelicited edema estimator 2604. The example of “machine learning” is usedherein as an example of a method of creating an estimator, and shouldnot be considered limiting. Alternatives include, for example generalpurpose statistical methods and/or estimator definition based ontheoretical equations accounting for observed correlations. In someembodiments, training set data used in generating the learned elicitededema estimator 2604 are obtained at least in part in vivo. In someembodiments, at least part of the training set data used to generate thelearned elicited edema estimator are obtained in vitro, for examplebased on ablation and/or mechanical stimulation of porcine heart wall.

Feedback Inputs to Machine Learning of an Edema Estimator

In some embodiments of the invention, for each collection of elicitededema parameters 2500 in the training set, there is also provided asinput to block 2603 a corresponding data collection indicating observededema states 2602. For example, the observed edema states optionallyincludes a measurement showing one or more of the following potentialmanifestations of edema: impedance and/or impedance dynamics, contactpressure, location of the eliciting stimulus (also referred to herein asa tissue “insult”), duration and/or power of the eliciting stimulus,lesion assessment or another tissue characteristic or any otherparameter relating to an ablation operation which potentially also maymanifest as edema.

In some embodiments, edema is observed by measurements of electricalsignal through a tissue region, wherein there is no, attenuated, and/ordynamically attenuating electrical signal (e.g., reduction of electricalimpulse transmitted across a pulmonary vein isolating ablation linecomprising potentially edematous tissue), but this is not explained byfull ablation (e.g., two ablations flanking the region of blockedtransmission are apparently insufficiently close to comprise a permanentimpulse blockade). Optionally, this measure of edema is confirmed by alater measurement (particularly for use in assembling a training set foruse by machine learning 2603); for example, assessment of later recovery(or lack thereof), e.g., 1 month or more after edema was originallyelicited.

In some embodiments, data indicating observed edema states are obtainedby the same catheter that was used to form the ablation. For example, aprobe of an RF ablation catheter is operated to ablate tissue, and thenelectrodes of the same catheter are used to measure potential electricalfields (e.g., high frequency electrical fields) induced in the vicinityof the ablation. From these measurements, impedance propertiesindicating edematous state are optionally calculated.

In some embodiments, the impedance properties in turn indicatedielectric properties of tissue that are changed as a result of tissueablation. Dielectric properties and/or impedance are optionallyinterpreted as indicating local tissue state and in particular, localtissue state(s) as being permanently ablated (e.g., converted tofibrotic tissue), edematous but not fibrotic, and/or healthy. Dataprovided as a collection of observed edema states 2602 is optionallyexpressed in any suitable format; for example: voltages, impedances,dielectric properties, and/or tissue state(s) inferred therefrom.Optionally, another measure of observed edema states is provided, forexample, imaging results, and/or direct thickness measurements(applicable in the case of in vitro experiments, for example).

In some embodiments dynamics of measurements such as impedancemeasurements are used. For example, impedance at an ablation region orregion of mechanical contact is optionally set to a baseline beforeedema begins (e.g., before, during and/or immediately after ablation).During edema development, changes in impedance are measured (e.g., atfrequencies up to about 1 MHz), and these changes optionally serve asinput to an elicited edema estimator. The relevant dynamics areoptionally provided in terms of magnitude, rate (slope and/or exponent,for example), and/or in terms of function shape, for example, linear,exponential, logarithmic or another shape.

In some embodiments, the impedance measurements include measurements ofimpedance between different electrodes on a probe of the ablationcatheter or other probe (e.g., between a tip electrode of the ablationcatheter and another electrode on the same catheter), between one ormore electrodes on the ablation catheter and one or more electrodes onanother catheter, and/or between one of more of the ablation electrodesand one or more body surface electrodes. In some embodiments, theimpedance measurements include measurement of impedances at variousfrequencies, e.g., from about 10 kHz to about 1 MHz.

Optionally, estimated edema states 2606 are accompanied by a metric ofcertainty, for example, sensitivity and/or selectivity. In someembodiments, estimated edema states 2606 are displayed within aninteractive user interface facilitating use by an operator to examinethe estimate of edema, from different perspectives: for example, tocheck expected time course and/or distance of spread. In someembodiments, estimated edema states may be displayed as a map (to forman edema map, that is a representation of a surface showing positions oftissue which has entered a state of some degree of edema, optionallyincluding representation of the degree of edema) on tissue regions,optionally as a function of current time any selected future time, or acombination of the two (e.g., an edema map optionally indicates bothwhere edema is, and when and/or where it is currently expected toarise).

Model Input to an Edema Estimator

In some embodiments, the machine learning of block 2603 proceeds on thebasis of an assumed model of edema 2601.

In some embodiments, sub-lesion modeling for the triggering of edemaelicited by ablation is based on temperature modeling (for examplecombined EM and thermal modelling).

In some embodiments, parameters used in the temperature modeling includemodeling of deformation of a contacted tissue region, e.g., in responseto relative ablation probe angle and/or contact/pressure.

Other model parameters optionally include dielectric and/or thermalmodeling of the body shape and internal organs including heart; as wellas electrical energy transmission parameters of an ablation ground padand electrodes of a catheter.

The model, in some embodiments, applies the EM and thermal model tosimulate application of energy to an ablation area based on selectedpower, time, and/or EM frequency (e.g., around 460 kHz) parameters.Optionally, the EM and thermal model operates iteratively in time steps,e.g., of 100 msec, 250 msec, 500 msec, 1 sec, or another larger, smallerand/or intermediate time step. Simulated temperature changes result frompower loss density conversion to thermal energy. In some embodiments,electrical properties which are themselves a function of temperature arealso adjusted; for example, conductivity and relative permittivity. Insome embodiments, changes to these properties are about 2%/° C.Optionally, thermal properties (e.g., specific heat and/or thermalconductivity) are also simulated.

Optionally, heated areas are divided into zones around the ablationregion, based on temperature distribution. Each zone has a differentgeometry, evolving differently over time in temperature as result ofdifferent heat distributions, initial morphology (tip angled flat orperpendicular, for example) and/or tissue properties.

In some embodiments, a temperature range reached during ablation is usedto distinguish fully ablated tissue from tissue which may go on tobecome edematous. For example, during ablation, tissue in a first zone(zone1) which reaches (and/or is modeled to reach under anticipatedconditions of ablation) a temperature of at least 55° C. is optionallyconsidered to be permanently non-conducting (and so, generally not inneed or revisiting, whether or not it experiences some subsequent degreeof edematous reaction).

Optionally, a distinction is made between two or more edema elicitingconditions experienced by tissue as the result of an ablation and/ormechanical contact. In some embodiments, the distinction is based onmaximum temperature achieved during an ablation. For example, a zone(zone2) surrounding a permanently inactivated zone (e.g., reachingtemperatures of 45° C.-55° C.) is distinguished from another zone (e.g.,zone3, reaching temperatures of 40° C.-45° C.). Time courses and/oramplitudes of edema are optionally modeled with different constants foreach of these two zones. Zone sizes are optionally determined bytemperature modeling. Optionally, zone sizes are set by an empiricallydetermined rule, e.g., the combined area of the two temperature zoneshas been found to be about twice as big as the corresponding size of thepermanently ablated zone they arise with, and having the same generalshape.

In some embodiments, zone1 and zone 2 (or any other zone defined) aredefined to differ from each other in one or more of edema lag time,rising phase time, and peak amplitude. The exact relative values chosenneed not be exactly corresponding to actual edematous time courses, asthey are often used to establish a “safety” function, within whichablation can proceed normally and with high chances of success, andoutside of which, ablation requires adjustment of parameters, and/or thechances of success are lowered, Optionally, zone2 edema (the sloweredema), is modeled to proceed with any of the time courses describedherein above. Optionally, zone1 edema is faster than this in anysuitable respect, for example, 10% faster, 20% faster, 30% faster, orfaster by another larger, smaller, and/or intermediate fashion.Optionally, the fully developed amplitude of elicited edema is maximalwithin zone1 (e.g., full block complete prevention of further ablation),but smaller within zone2, unless added to by further ablations. Forexample, zone2 is optionally modeled as having reaching a maximum edemahaving half the power to temporarily block impulses and/or interferewith ablation as zone1 reaches, or any other suitable ration such as30%, 60%, 80%, or another larger, smaller, or intermediate number.

Over time (e.g., a month after ablation), the directly ablated zone hasbeen observed in in vivo animal model preparations to tend to shrink byabout 15%-20%, while zone2 and zone3 recover to their originalthickness.

In some embodiments, more zones are defined. Optionally, edema elicitingconditions are modeled continuously, with corresponding continuousgradations in time course and/or amplitude constants.

Learned Edema Estimator

After the above-described inputs (observations of edema states 2602,parameters related to edema production 2500, and edema model 2601) aresuitably defined and received for a plurality of elicited edemainstances (e.g., 50, 100, 1000, or another larger, smaller, orintermediate number of elicited edema instances), machine learning atblock 2603 uses of one or more machine learning methods, to produce alearned elicited edema estimator 2604. Examples of machine learningmethods used in some embodiments of the present invention include, forexample: decision tree learning, association rule learning, anartificial neural network, deep-learning artificial neural network,inductive logic programming, a support vector machine, cluster analysis,Bayesian networks, reinforcement learning, representation learning,similarity and metric learning, and/or another technique taken from theart of machine learning. Machine learning at block 2603 may be inaccordance with techniques described in relation to machine learning1603 and/or machine learning 2103.

In some embodiments, zones or other spatially characterizeddistributions of thermal initiation of edema are converted into expectedtime courses of edema development, e.g., time courses derived frommachine learning (block 2603) of observed associations of elicited edemainstances with elicited edema parameters. Use of machine learning, insome embodiments, comprises refinement of the basic model of edemadescribed here, e.g., learning that assigns values adjusted from one ormore of the default values described herein, based on particulars of theablation scenario, in order to more accurately predict outcomes.

In some embodiments, this comprises assignment edematous time courses totissue based one modeled temperature zone. Moreover, based onobservations in the literature, an instance of edema may be expected todevelop with a sigmoidal time course; i.e., initially slowly developing,followed by a rapid phase of edema development, leveling gradually offto a fully developed edematous state. A typical overall time course hasbeen observed to be about 30 minutes to fully developed edema (e.g.,ablation to at least 98% fully developed); optionally another total timecourse duration is used, for example, 20 minutes, 25 minutes 35 minutes,40 minutes, or another larger, smaller and/or intermediate time.Optionally, termination of lag phase is defined with respect to a cutoffof about 10% of maximum amplitude, and termination of the rising phasedefined with respect to a cutoff of about 90% of maximum amplitude.Optionally, constants of the sigmoid are set so that lag phase ends atabout 1 minute 2 minutes after ablation, 3 minutes after ablation, 5minutes after ablation 8 minutes after ablation, 10 minutes afterablation, or another longer, shorter or intermediate time afterablation. Optionally, lag phase is defined to begin the same number ofminutes before the elapsing of about 30 minutes after ablation (oranother rise-time). Lag phase and rising phase are optionallysymmetrical in time. Optionally, they are asymmetrical (e.g., defined tohave different termination/onset times respectively, relative to timefrom the 50% amplitude point of rising phase) The amplitude may beconsidered to rise from 0 to 1, wherein 1 is a state of complete edema.Optionally, amplitude is expressed in terms of effects (and/or maximumeffects of edema referenced to current edema state, e.g., bymultiplication), e.g., how much more power and/or time should be used toablate in the edematous region successfully, last time post-ablationthat ablation should be attempted without revision of an ablation plan,etc. Optionally, a physician can set any desired threshold or otherfunction for these effects, based on their own preferences (which may bebased on their own style of ablation, tolerance for uncertainty ofresult in the estimator output, assessment of patient risk, etc.),Calibration of time course of edema, and/or effects of edema onestimators or other uses of edema estimators which may use them, can bedone by use of a test ablation with a subject (e.g., maximum edema iscorrelated to same-patient observations of ablation power absorption,etc.), avoiding a need for undue experimentation.

In some embodiments, predictions of edema include such observations inthe assumptions of model of edema 2601, used to structure machinelearning 2603. Sigmoidal constants are optionally defined to vary overthe surface of a tissue extending away from the region of initialinsult, e.g., according to temperature zone. Optionally, time courseparameters vary with tissue depth.

Any suitable number of constants are optionally used to express observededema time courses, e.g., one for amplitude and at least one more fordynamics (e.g., optionally, lag, rise, and leveling off are eachseparately assigned parameters; alternatively, two or more of these areoptionally described by a shared parameter) The time course of edemadevelopment (e.g., constants of a sigmoidal functioning describing edemadevelopment) is optionally variable for different tissue regions,different patients, and/or different states of the same patient. Edematime course can also vary depending on the degree of insult that elicitsit; for example, duration and power of ablation, maximum temperaturereached (e.g., as just described) and/or contact pressure. In someembodiments, edema time course for a patient is calibrated based onmeasurements of actual edematous response to lesions made. Optionally,calibration is based on any partial or complete edema response (e.g.,edema observed up to the end of a lag period, edema observed during therapid rising phase, and/or edema observed during a levelling-offperiod). Optionally, an ablation “test lesion” is made at some isolatedportion of the heart wall, and periodically observed. Optionally, anysub-lesion an ablation line (e.g., an initial sub-lesion) is measuredfor calibration. Optionally, calibrations are performed per region,and/or per region type (e.g, separately for thicker and thinner heartwall regions).

In some embodiments, the learned elicited edema estimator 2604 comprisesa set of learned weights applied to terms of model of edema 2601, anequation, or an elicited edema estimator function in another suitableform. Application of the edema estimate at block 2605, in someembodiments, comprises plugging into the model appropriate values from acollection of elicited edema parameters 2500 for some new instance ofedema, and calculating the result. The result is produced as anestimated edema state 2606, applied to an affected region of tissue, andoptionally evolving over time for each particular instance of edema.

In some embodiments, estimated edema states 2606 are separatelydetermined for a plurality of instance of edema time courses, which areoptionally at least partially overlapping in space and time. Optionally,edema response for a particular region is according to the worst-case(largest/fastest edema) for any potentially contributing instance ofedema. In some embodiments, effects are additive, optionally additivewith a suitable adjustment (e.g., proportional decreases in additiveamplitude) for a maximum-limited state of edema.

In some embodiments, estimated tissue state for a period afterresolution of edema is also calculated from based on the elicited edemainstances result, for example, by reverting the electrical properties ofzone2 and zone3 to their pre-ablation states, while continuing to modelzone1 as irreversibly ablated. Optionally, zone1 is further modifiedfrom an initial value (e.g., as initially reconstructed from a CT image)to reflect shrinkage (e.g., shrinkage of 15-20%).

In some embodiments, results of the immediate, time-evolving estimatededema state 2606 and/or estimated long-term state modeling are used asinputs to another estimator, for example, an ablation segmenteffectiveness estimator as described in relation to FIG. 14A, or anotherestimator, for example, a lesion estimator as described in relation toFIG. 1B or ablation line estimator as described in relation to FIG. 6 .

In some embodiments, an elicited edema estimator and/or anotherestimator applied to a sequence of sub-lesions and/or ablation segmentsis used to assist a physician in defining a line of planned ablations;for example, optimizing the line of planned ablations to a minimallength and/or minimal number of ablation needed to isolate one or morefeatures such as vein roots which a preliminary line of planned ablationsurrounds, and/or which another indication by the physician otherwisedelineates, indicates, and/or selects.

In some embodiments, results of the immediate, time-evolving estimatededema state 2606 and/or estimated long-term state modeling are used asinputs to another estimator or computer hardware for planning anablation path, e.g., an optimal ablation path according to one or morecriteria. Such computer hardware and/or methods are also described forexample, in International Patent Publication No. WO2016/181317; thecontents of which are included herein by reference in their entirety.

In some embodiments, results of the immediate, time-evolving estimatededema state 2606 and/or estimated long-term state modeling are used asinputs to another estimator or computer hardware for adjusting apre-planned an ablation path, e.g., it may be used to add or reduceablation points (or segments). Optionally, such adjusting is performedon-line during treatment.

In some embodiments, edema map is used as input to another estimator orcomputer hardware for planning and/or adjusting an ablation path.Optionally, such adjusting is performed on-line during treatment.

In another example, an initially proposed ablation path optionallydefines 60 planned ablation regions (sub-lesions). Upon suitableapplication of an elicited edema estimator and/or ablation segmenteffectiveness estimator to the purpose, a system optionally reportswhether or not the path as defined will create a closed ablation line.The system optionally also advises if there is a shorter and/or simplerablation line which could successfully create a similarly effectiveclosed ablation line, for example, using only a sub-group of 40 ablationregions. Optionally, the system advises if there are additional ablationregions that should be added for a higher likelihood of ablation lineclosure.

Potentially, this saves ablating (and/or re-ablating) regions which arenot required, and/or strengthens ablation segments which requireadditional ablation, for example by reducing distance between ablationregions. Optionally, such revision is performed on-line duringtreatment, e.g., in response to actual positions of sub-lesions.

Physiological Simulation—Example of Display of Edema Simulation Results

Reference is now made to FIGS. 17A-17D, which schematically representindicating changes to the display of a rendered tissue region 2050 dueto predicted and/or measured edema, according to some embodiments of thepresent disclosure.

In some embodiments, edema development (predicted and/or measured) isshown to an operator (e.g., a physician) in real time, during performingof a procedure such as ablation procedure, or another procedure liableto elicit edema. In some embodiments, edema development (predictedand/or measured) is shown to an operator (e.g., a physician) in realtime in a form of edema map.

A potential advantage of real time prediction and/or display of edemadevelopment is that it may encourage an operator to ablate faster (e.g.,at higher power) when edema is seen to and/or predicted to developquickly.

Real time prediction and/or display of edema development potentiallyalso guides a physician in the choice of ablation parameters. E.g., ifedema from a preceding sub-lesion moves fast towards another targetedregion for sub-lesion placement, an operator may change parameters toachieve full ablation faster, change the direction of the catheter, etc.

Optionally, an edema map assists an operator in choosing a nextplacement of a sub-lesion. Optionally, given a planned placement for asub-lesion, an edema map assists in setting ablation parameters. In someembodiments, the system itself calculates and suggests a next sub-lesionplacement and/or ablation parameters based in part on the edema map.

In FIG. 17A, lesion 2401 represents a recently formed lesion, forexample, an RF ablation lesion. Over the course of a few minutes afterRF ablation, tissue potentially reacts with an edematous swellingresponse. Optionally, edema is elicited by another even during aprocedure, for example, mechanical contacts between a catheter and atissue surface.

In some embodiments of the invention, the swelling response is simulated(for example, as a function of time, and/or based on measurements suchas dielectric and/or impedance measurements that provide edema data) byone or both of increasing thickness in a region 2403 surrounding lesion2401, and a change in color and/or texture in region 2402 (representedby the partial rings in the drawing). Thickness changes can also be seenin the changing (increasing) thickness of region 2411 between FIGS.17B-17D; comparison also can be made to the baseline surface boundary2050A.

In some embodiments, another representation of edema is usedadditionally and/or alternatively, for example, changes in color,texture, or another visual feature.

General

As used herein with reference to quantity or value, the term “about”means “within ±10% of”.

The terms “comprises”, “comprising”, “includes”, “including”, “having”and their conjugates mean: “including but not limited to”.

The term “consisting of” means: “including and limited to”.

The term “consisting essentially of” means that the composition, methodor structure may include additional ingredients, steps and/or parts, butonly if the additional ingredients, steps and/or parts do not materiallyalter the basic and novel characteristics of the claimed composition,method or structure.

As used herein, the singular form “a”, “an” and “the” include pluralreferences unless the context clearly dictates otherwise. For example,the term “a compound” or “at least one compound” may include a pluralityof compounds, including mixtures thereof.

The words “example” and “exemplary” are used herein to mean “serving asan example, instance or illustration”. Any embodiment described as an“example” or “exemplary” is not necessarily to be construed as preferredor advantageous over other embodiments and/or to exclude theincorporation of features from other embodiments.

The word “optionally” is used herein to mean “is provided in someembodiments and not provided in other embodiments”. Any particularembodiment may include a plurality of “optional” features except insofaras such features conflict.

As used herein the term “method” refers to manners, means, techniquesand procedures for accomplishing a given task including, but not limitedto, those manners, means, techniques and procedures either known to, orreadily developed from known manners, means, techniques and proceduresby practitioners of the chemical, pharmacological, biological,biochemical and medical arts.

As used herein, the term “treating” includes abrogating, substantiallyinhibiting, slowing or reversing the progression of a condition,substantially ameliorating clinical or aesthetical symptoms of acondition or substantially preventing the appearance of clinical oraesthetical symptoms of a condition.

Throughout this application, embodiments of this disclosure may bepresented with reference to a range format. It should be understood thatthe description in range format is merely for convenience and brevityand should not be construed as an inflexible limitation on the scope ofthe disclosure. Accordingly, the description of a range should beconsidered to have specifically disclosed all the possible subranges aswell as individual numerical values within that range. For example,description of a range such as “from 1 to 6” should be considered tohave specifically disclosed subranges such as “from 1 to 3”, “from 1 to4”, “from 1 to 5”, “from 2 to 4”, “from 2 to 6”, “from 3 to 6”, etc.; aswell as individual numbers within that range, for example, 1, 2, 3, 4,5, and 6. This applies regardless of the breadth of the range.

Whenever a numerical range is indicated herein (for example “10-15”, “10to 15”, or any pair of numbers linked by these another such rangeindication), it is meant to include any number (fractional or integral)within the indicated range limits, including the range limits, unlessthe context clearly dictates otherwise. The phrases“range/ranging/ranges between” a first indicate number and a secondindicate number and “range/ranging/ranges from” a first indicate number“to”, “up to”, “until” or “through” (or another such range-indicatingterm) a second indicate number are used herein interchangeably and aremeant to include the first and second indicated numbers and all thefractional and integral numbers therebetween.

Although the invention has been described in conjunction with specificembodiments thereof, it is evident that many alternatives, modificationsand variations will be apparent to those skilled in the art.Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

All publications, patents and patent applications mentioned in thisspecification are herein incorporated in their entirety by referenceinto the specification, to the same extent as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated herein by reference. Inaddition, citation or identification of any reference in thisapplication shall not be construed as an admission that such referenceis available as prior art to the present invention. To the extent thatsection headings are used, they should not be construed as necessarilylimiting.

It is appreciated that certain features, which are, for clarity,described in the context of separate embodiments within the presentdisclosure, may also be provided in combination in a single embodiment.Conversely, various features, which are, for brevity, described in thecontext of a single embodiment, may also be provided separately or inany suitable subcombination or as suitable in any other describedembodiment of the present disclosure. Certain features described in thecontext of various embodiments are not to be considered essentialfeatures of those embodiments, unless the embodiment is inoperativewithout those elements.

What is claimed is:
 1. A method of estimating joint effectiveness of aplurality of tissue-ablating operations, the method comprising:receiving data indicative of parameters of the plurality oftissue-ablating operations, the data including: data indicative ofdistance between sub-lesions formed by the tissue-ablating operations,and data indicative of an interval of time between the tissue-ablatingoperations; estimating, based on the received data and using computercircuitry, the joint effectiveness of the plurality of tissue-ablatingoperations; and displaying an indication of at least one of: theestimated joint effectiveness, and an adjustment to furthertissue-ablating operations, the adjustment being determined according tothe estimated joint effectiveness.
 2. The method of claim 1, wherein thedisplayed indication provides an indication of the likely effectivenessof the tissue-ablating operations to prevent post-block recurrence ofmyocardially-conducted electrical impulse transmission across the regionof tissue extending between a plurality of sub-lesions formed by thetissue-ablating operations.
 3. The method of claim 1, wherein thedisplayed indication provides an indication of a need to perform afurther tissue-ablating operation to prevent post-block recurrence ofmyocardially-conducted electrical impulse transmission across the regionof tissue extending between a plurality of sub-lesions formed by thetissue-ablating operations.
 4. The method of claim 1, wherein theinterval of time is indicative of a state of edema elicited by anedema-inducing operation performed earlier than the latest of theplurality of tissue-ablating operations.
 5. The method of claim 4,wherein the state of edema is estimated based on a time since anedema-inducing operation comprising one of the tissue-ablatingoperations, and indicated by the interval of time.
 6. The method ofclaim 1, wherein the estimating comprises estimating joint effectivenessthe ablation operations are expected to have after the estimating, andat least half an hour after the tissue-ablating operations.
 7. Themethod of claim 6, wherein the estimating is for joint effectiveness ofthe tissue-ablating operations after the estimating, and following aperiod of recovery from the tissue-ablating operations.
 8. The method ofclaim 6, wherein estimating is of the joint effectiveness of theablation operations at least one week after the tissue-ablatingoperations and the estimating.
 9. The method of claim 1, wherein thereceived data comprise data indicative of dielectric measurements of theablated tissue.
 10. The method of claim 9, wherein the dielectricmeasurements are indicative of tissue thickness at locations of thesub-lesions.
 11. The method of claim 9, wherein the dielectricmeasurements are indicative of tissue ablation in the sub-lesions. 12.The method of claim 1, wherein the received data comprise parameters ofoperation of an ablation device during the tissue-ablating operations.13. The method of claim 12, wherein the parameters of operation of theablation device comprise any one or more of the duration and power ofenergy delivery to the sub-lesions.
 14. The method of claim 12, whereinthe parameters of operation of the ablation device comprise any one ormore of contact force and dynamics of contact of an ablation probe ofthe ablation device with tissue at the sub-lesions.
 15. The method ofclaim 1, wherein the received data also include measurements of any oneor more of temperature and impedance drop measured at the ablatedtissue.
 16. The method of claim 1, further comprising indicating theestimated joint effectiveness; wherein the indicating is beforecompletion of planned tissue-ablating operations planned to complete anablation line comprising the sub-lesions.
 17. The method of claim 1,wherein the received data also include indications of tissue wallthickness at locations of the sub-lesions.
 18. The method of claim 1,wherein the locations of the sub-lesions comprise locations of thecardiac wall.
 19. The method of claim 1, wherein the plurality oftissue-ablating operations generates a plurality of sub-lesions, andestimating the joint effectiveness of the plurality of tissue-ablatingoperations comprises using both individual lesion parameters for saidsub-lesions and lesion interaction parameters for one or more pairs ofsub-lesions.
 20. The method of claim 19, wherein each individual lesionparameter indicates how the sub-lesion is planned to be formed, how thesub-lesion is actually formed, and/or a characteristic of the respectivesub-lesion.
 21. The method of claim 19, wherein the individual lesionparameter indicates measurements of the sub-lesion at a plurality oftimes during and/or after the formation of the sub-lesion.
 22. Themethod of claim 19, wherein said estimating the joint effectivenesscomprises calculating a function of said individual lesion parametersand said lesion interaction parameters.
 23. The method of claim 19,wherein said estimating the joint effectiveness comprises a chainedestimation of first estimating individual lesion effectiveness and thenestimating lesion interaction effectiveness based thereon.
 24. Themethod of claim 19, wherein the estimating of joint effectiveness isaffected by a plurality of the parameters in combination, such that forat least some combinations of values of those parameters, no singleparameter is itself sufficient to yield the value of the estimating. 25.The method of claim 1, wherein estimating comprises estimating based ona shape of a lesion formed or to be formed, by said tissue-ablatingoperations.
 26. The method of claim 1, wherein estimating comprisesestimating based on a temporal order of said tissue-ablating operations.27. The method of claim 1, wherein estimating comprises estimating aneffect of a potential error and/or a likelihood of an error in applyingsaid tissue-ablating operations.
 28. The method of claim 1, whereinestimating comprises estimating during applying said tissue-ablatingoperations and modifying an ablation plan according to results of saidestimating and estimation of a time to perform further such tissueablation operations.
 29. The method of claim 1, wherein estimatingcomprises estimating an expected location of failure of saidtissue-ablating operations.
 30. The method of claim 29, whereinestimating an expected location of failure comprises taking patientanatomy into account.
 31. The method of claim 1, wherein estimatingcomprises estimating using a lesion parameterization array.
 32. Themethod of claim 1, wherein estimating comprises estimating a pluralityof different effectiveness measures.
 33. The method of claim 1, whereinsaid data indicative of distance comprises geometrical data.
 34. Themethod of claim 1, wherein said data indicative of distance comprisesdata from an estimation of real or effective distance or a measurementof ablation locations.
 35. The method of claim 1, wherein none of saidreceived data provides by itself an estimate of the joint effectiveness.36. The method of claim 1, wherein the plurality of tissue-ablatingoperations include more than two tissue-ablating operations, and thedata indicative distance between sub-lesions include data indicative ofa plurality of distances.
 37. The method of claim 1, wherein theestimating incorporates the interval of time such that the jointeffectiveness varies among a plurality of decreasing values withincreasing interval of time between first and second sub-lesions of thesub-lesions.
 38. The method of claim 37, wherein the estimated jointeffectiveness of a longer interval and a shorter distance betweensub-lesions is equivalent to the estimated joint effectiveness of ashorter interval and a longer distance between sub-lesions.
 39. Themethod of claim 1, wherein the estimating uses an estimator createdusing machine learning.
 40. The method of claim 1, wherein thedisplaying an indication displays the adjustment to furthertissue-ablating operations, determined according to the estimated jointeffectiveness.